\documentclass{buIE47x}
% Use in paper preamble \addtitlepagetop, \addtitlepagebottom, \addtitlepageleft, \addtitlepageright with negative lengths
% (e.g., \addtitlepagetop{-1cm}) to enlarge titlepage margins if necessary (e.g., to fit all material into one page)

\addtitlepagetop{-1cm} \projectname{Income Prediction Modelling Project}

\company{Hayat Finans}

\teamno{2}

\projectshortcode{HYFGT}

\teammembers{%
	Nazlıcan Akman \\
	Orkun Arslan \\
	Arda Noyan Karaşoğlu \\
	Göze Yılmaz \\
	Şima Ünver \\
	Selvi Ecem Civelek 
}

% Industrial advisors
% Use optional form \IA[team]{...} if you have two or more IAs
\IA{%
	Alperen Doğan, Hayat Finans,\\
	Credit Analytics Specialist
}

% Academic advisor(s)
\AA{%
	Prof.\ Dr.\ Savaş Dayanık, Bilkent University\\
	Department of Industrial Engineering
}


\abstract{
	This project aims to provide more accurate revenue forecasts by expanding customer revenue beyond relying solely on income statements and e-Government information, incorporating additional revenue items and locations of residence. The ability to forecast revenue beyond customer statements is critical in the banking sector, as revenue directly impacts creditworthiness and the value of every product a bank can offer. Inaccurate revenue forecasts lead to dissatisfaction among customers with insufficient creditworthiness, increased fraud risks, and the negative impact on bank revenue from customers who are actually solvent but denied credit due to inaccurate income forecasts. To calculate more accurate revenue forecasts, the model we developed examines the correlation between specific customer characteristics that can be linked to revenue and analyzes the impact of location as a new characteristic. Ultimately, the overall revenue forecasting model aims to increase bank revenue while simultaneously reducing risks and fraud attacks and preventing long-term improvements in the customer experience, such as e-Government integration. 
}

\keywords{
	E-Government integration, residence location, income prediction, creditworthiness, risk, fraud attacks, customer features
}

\bibfilename{report.bib}

\begin{document}
\clearpage
\section{Description of the System}
\label{sec:Description of the System}
Hayat Finans is the first digital bank in Turkey, offering financial services to both individual and corporate consumers through mobile applications and internet banking since 2022.  The institution does not currently offer financial financing. Since beginning its operations in 2023, Hayat Finans has acquired more than 400,000 individual customers and over 3,200 corporate clients within just one year, becoming Turkey’s largest digital bank in terms of total assets (Hayat Finans, 2025). Instead, it offers deposit account facilities through which customers may obtain purchase credits and settle credit loans directly via authorized dealers. The company determines the credit limit based on the customer's income, which is automatically retrieved from the system through the mobile application's integration with the E-Government platform. In addition to income verification, each applicant undergoes a comprehensive examination based on their credit information retrieved from the Credit Registry Bureau (KKB), which includes their credit history, existing obligations, and payment performance. The current income model is vulnerable to misleading income information due to the absence of rent or unverified income on the E-Government platform, despite the implementation of digital security measures.

\section{Current System Analysis and Problem Definition}
\label{sec:Current System Analysis and Problem Definition}
The following information was gathered from a meeting with Hayat Finans's credit analytics specialist, Alperen Doğan, regarding the current system and the project's aims and expectations.

\subsection{Current System Analysis}
\label{sec:Current System Analysis}
Operating entirely through digital channels, Hayat Finans conducts its credit evaluation process without any physical interaction, relying instead on digital systems for income verification. In the current structure, the E-Government integration is the only method by which user income information is accessed, as it retrieves the SGK 4A, 4B, and 4C employment records. This record includes the customer's employer, salary amount, and insurance contributions. However, it does not include any additional income sources, such as rental revenue, freelance income, investment returns, or property ownership information that could indicate potential real estate income. Consequently, the customer's actual income level frequently appears to be lower than it is in reality. Since requesting payslips or supporting documents manually would slow down the process, creating friction in the customer journey, it is generally avoided in practice. Therefore, the existing mechanism operates based on a single, static source of salary information.

This income data is supplemented by information obtained from the Credit Bureau of Turkey during the credit assessment process. The credit bureau data set includes the customer's payment history, credit card limits, current debt status, and a fraud score that indicates the probability of fraudulent activity or identity misuse. The bank rejects applications that are considered as too risky when these credit and fraud scores are compared to specific limits established by sectoral laws and internal risk policies. Using a customer’s verified income, existing credit limits at other institutions, and the sector-based lending cap that restricts total borrowing relative to income, these thresholds indicate the minimum level of credit reliability required for loan approval. Currently, however, such scores and simple rule-based comparisons remain the sole methods for detecting fraudulent cases. Advanced anomaly detection or behavioral analysis tools are not used.  Furthermore, the information about people's backgrounds, where they live, their family income, and their deposit amounts are not yet part of the assessment process. Therefore, Hayat Finans's current system for verifying income is a rule-based, single-source system that doesn't integrate data very well. This makes it harder to get accurate estimates of income and leaves the process open to scam risks. When these credit and fraud scores are compared using Hayat Finans’ embedded system to certain limits set by sectoral laws and internal risk policies, the bank rejects applications that are too risky \cite{BDDK2022}. Based on a customer's confirmed income, total current credit limits across other banks, and the sectoral lending cap that limits total credit exposure compared to income, these boundaries show the lowest level of creditworthiness needed to get a loan \cite{BDDK2022}. At the moment, though, these scores and basic rule-based matching are the only ways to spot scam. Advanced anomaly detection or behavioral analysis tools are not used. Additionally, information about customers' backgrounds, where they live, their family income and the amount of their deposits are not yet part of the evaluation process. Therefore, Hayat Finans's current system for verifying income is a rule-based, single-source system that doesn't integrate data very well.This leaves the process open to fraud and makes it challenging to get precise income estimates.

\subsection{Problem Definition}
\label{sec:Problem Definition}
The current income verification system of Hayat Finans relies predominantly on E-Government integration, which retrieves only SGK 4A, 4B and 4C employment records showing formal salary information. Although this provides a guaranteed income prediction data, many potential customers are lost at this process of the customer journey due to the longevity and lack of reliance on this system. This results in decreased customer retention and satisfaction rates, as well as increased fraud attacks, which decrease the company's liabilities due to the loss of opportunities. Moreover, this decreases the accuracy and efficiency of the customer segmentation system, which aims to enable a tailored experience for customers based on their income.

\begin{figure}[!ht]
	\centering
	\includegraphics[width=0.95\textwidth]{figures/fig1.png}
	\caption{Fishbone Diagram outlining the root causes of inaccurate income estimation and the downstream effects on fraud risk, revenue loss, and segmentation in the current system.}
	\label{fig:1}
\end{figure}
\newpage
In conclusion, the key problems are:
\begin{itemize}
	\item The customer's declaration of income can be inaccurate, which may lead to fraud risks.
	\item The revenue estimation for customers is based solely on guaranteed data retrieved from E-Government, which reduces the offerable loan values calculated for each customer.
	\item The estimated income level using the current model and data is not accurate enough and is significantly underestimated, which results in an undercalculated potential liability.
\end{itemize}

These problems cause inefficiencies in three areas that are essential for profit generation, customer retention, and acquisition:

\paragraph{Data analysis:} The data retrieved from E-government, KAPS and the Credit Bureau is not analyzed up-to-standard based on their significance on income. There are many data points generated such as age, number of credit cards and payment status of different term loans. Calculating the impact on this data and making predictions based on their importance allows more accurate income prediction compared to the underestimated income based on the 4A, 4B and 4C documentations on E-Government as well as the customer declaration of income and hence decreases the risk of fraud attacks and default risks and increases the accuracy of loan offers and performance of the loans tailored to customers.

\paragraph{Customer Segmentation:} The bank implements a customer segmentation system that classifies consumers according to their income levels. This allows them to make profitable loan offers and efficient decision-making processes to increase customer retention and satisfaction. Accurate income prediction is the core of this segmentation, as it enables making the most profitable decisions and financial choices.

\paragraph{Revenue Optimization:} The proposed model will support the company in achieving sustainable loan portfolio growth and revenue maximization, while at the same time ensuring compliance with responsible lending standards and enhancing the reliability of revenue forecasts. Instead of relying on a lower-bound income forecast, it bases judgments on full data, which helps with the risk-reward balance. Knowing the full financial strength and credibility of a customer enables optimizing the revenue and accurate resource allocation.

\section{Review of Resources}

The main objective of the project is estimating customer total income based on specific features such as age, educational level, marital status, place of residence, previous credit information etc. to help maximizing the bank’s revenue while minimizing default risk. A variety of Machine Learning algorithms can be used to process the data and gain insight to make predictions with maximum accuracy. This section discusses different methods usable for this project and their relevance. The selection has been provided as the total data is yet to be provided, forcing us to plan ahead for any instances.

\subsection{Literature Review}
Methods for fixing income prediction algorithms and pertinent industry standards are covered in this section.

\subsubsection{Benchmarking Credit Scoring Models}
We attempted to define our method based on a study that comprehensively evaluated various classification algorithms used for credit risk estimation. In a parallel research to our project problem they examined both homogeneous and heterogeneous ensemble methods, as well as individual classifiers such as logistic regression, decision trees, gradient boosting, and support vector machines. Their research revealed that ensemble methods are more effective than other algorithms, particularly in terms of accuracy and robustness. Furthermore, this outcome impacts the project's revenue forecasting model, indicating that ensemble methods enhance precision and reliability by integrating the features of multiple algorithms \cite{Lessmann2015}.

\begin{figure}[!ht]
\centering
\includegraphics[width=0.95\textwidth]{figures/fig2.png}
\caption{Classifier Development and Evaluation Process}
\label{fig:2}
\end{figure}
\newpage
\subsubsection{Model Fitting Process with Machine Learning}
One of our goals is to create a dynamic model that makes accurate revenue predictions by analyzing the impact of each feature in the data on revenue. To make this model dynamic, we need to test the accuracy of different machine learning algorithms that handle classification. We also examined the logic and comparisons of key machine learning methods for revenue prediction and took these into account as we progressed our project \cite{jo2024income}.

\paragraph{Logistic Regression:}  Logistic regression models the probability that an input belongs to a certain class such as a range of income. It uses the sigmoid function to map predictions to values between 0 and 1. The model first calculates a logit (log-odds) as a linear combination of input features and their coefficients\cite{jo2024income}. The mathematical expression is as follows:

\begin{align*}
	\theta = \log\!\left( \frac{\hat{p}}{1 - \hat{p}} \right)
	= \beta_0 + \beta_1 X_1 + \beta_2 X_2 + \dots + \beta_n X_n
\end{align*}

\paragraph{K-Nearest Neighbor:} K-Nearest Neighbor is a non-parametric method that classifies a data point according to the majority class of its K nearest neighbors in the feature space. Euclidean distance is the standard method for measuring distance. The choice of K is critical—too small leads to noise sensitivity, too large oversmooths decision boundaries \cite{jo2024income}. That is:

\begin{align*}
	X_{\text{new}} = \frac{X - \min(X)}{\max(X) - \min(X)}
\end{align*}

\paragraph{Decision Tree Pruning:} Decision trees recursively partition the data by selecting split conditions at each node to create homogeneous subsets. Data can be fit to tree forms not only for random tree pruning but also for other forest-based algorithms using one hot encoding. One hot encoding allows us to analyze the features on the data as binary variables. That is “1 if an entity has a feature in a certain manner, 0 otherwise”. The algorithm selects splits that minimize impurity. Trees naturally grow complex and overfit, so pruning is essential \cite{jo2024income}.

\paragraph{Random Forest:} Random Forest is an ensemble method that generates numerous decision trees from bootstrap samples (random selection with replacement) of the training data. Diversity is guaranteed by the use of a random subset of features at each division to construct each tree \cite{jo2024income}. The visual representation of this model is as shown in \ref{fig:3}.

\begin{figure}[!ht]
	\centering
	\includegraphics[width=0.95\textwidth]{figures/fig3.png}
	\caption{Structure of the Random Forest Model}
	\label{fig:3}
\end{figure}

\paragraph{Support Vector Machine:} SVM transforms input data into a high-dimensional feature space where it constructs a hyperplane that separates classes with maximum margin. The key innovation is the kernel function $K(x, x') = \langle \phi(x), \phi(x') \rangle$, which computes inner products in feature space without explicit transformation. The RBF (Radial Basis Function) kernel $K(x_i, x_j) = \exp(-\gamma \lVert x_i - x_j \rVert^2)$ measures exponential decay of distance between points, allowing non-linear decision boundaries. "Support vectors" are the nearest data points to the hyperplane that define its position. The cost parameter C balances fitting accuracy against model complexity \cite{jo2024income}. The overall accuracy of each model on predictions for each data can be seen in \ref{fig:4}.

\begin{figure}[!ht]
	\centering
	\includegraphics[width=0.95\textwidth]{figures/fig4.png}
	\caption{Models and Accuracy Rates}
	\label{fig:4}
\end{figure}

\subsubsection{Prediction of Individual Income Using Machine Learning}
Machine learning-based approaches have been compared to classical regression methods (OLS, LASSO, Ridge) in income forecasting studies. A study showed that classical methods are inferior to Random Forest, Gradient Boosting, K-Nearest Neighbors (KNN), and ensemble methods in terms of accuracy \cite{matkowski2021}. Particularly, the Random Forest and Gradient Boosting algorithms are recognized for their superior explanatory power in individual income forecasting, surpassing conventional models. This discovery is a strong endorsement of the utilization of machine learning-based methodologies in income forecasting projects and establishes a methodological foundation for enhancing the predictive accuracy of credit assessment processes \cite{matkowski2021}.

\subsubsection{Segmentation and Tree-Based Approaches for Income Prediction}
The article \cite{jafari2025selecting} presents a compact pipeline for predicting a financial target under incomplete tabular data, aligning with the aim of maximizing revenue through accurate income estimation. The workflow follows CRISP-DM: quantify and impute missing values, control outliers based on distribution (z-score for near-normal features, Turkey for skewed ones), and apply scaling for distance-sensitive methods. Modeling is framed along two routes: a single global regressor and a segmentation-then-model approach using k-means with silhouette selection to capture heterogeneous earning patterns. In comparative benchmarks, tree-based methods—especially CART—deliver leading accuracy over linear and KNN baselines, while preserving interpretability for risk governance. Overfitting is limited by depth and leaf constraints, minimal split sizes, pruning, and k-fold validation, as reported by MAE/MSE. Feature screening emphasizes practical financial drivers, such as work experience, age, other debts, and approved credit amount, which act as effective proxies when stated income is partial or unreliable \cite{jafari2025selecting}.

These elements translate into concrete contributions to the project: a defendable preprocessing sequence for incomplete income fields, a segmentation option that can reduce errors where customer profiles differ, tree-first baselines that balance accuracy with explainability, and an evaluation protocol that pairs holdout and cross-validation to demonstrate generalization. The deployment guidance—performance monitoring, drift checks, and scheduled refresh—supports stable operations after launch. Together, the article provides a ready-to-adopt methodological framework for an income estimator that accurately predicts customer incomes and informs banking system decisions with high accuracy and auditability.


\subsubsection{Correlation Between Rent Prices and Income}
A study conducted at Harvard Univerity supports our assumptions for the district-based location score scale we will use, has revealed the standard of 30\% of income spent on rent, which is widely used in the USA \cite{Herbert2018}. Although the study states that the 30\% standard is not fully appropriate and is misleading, especially in high-income markets and high-income, small households, it is still stated that "payment spent on rent" as a macro-level indicator is a useful and applicable metric for estimating total income. The study conducted at Lincoln Instution measures housing affordability using indicators such as "housing-price-income ratio" and "rent-income ratio" in a sample of 200 cities worldwide \cite{Kallergis2022}. The two main criteria developed by the study are as follows; One is occupant affordability analysis, which is the relationship between the cost of living in a particular dwelling and the ability of a median-income household to pay for two units in a typical private housing sector, and the other is by the Lincoln Institute of Land Policy, which shows that rents/expenses can be very high if the median price/income ratio is around 6.2. This approach also shows the significant impact of city density, distribution, land and zoning restrictions, etc., on housing distribution.

\subsubsection{Income Prediction of Urban and Rural Areas}
\cite{xiong2022income} conducted a study that introduced an income prediction model for urban and rural residents, employing a Backpropagation (BP) Neural Network optimized via the Particle Swarm Optimization (PSO) algorithm. The study emphasizes that income forecasting constitutes a nonlinear problem, and traditional BP models are susceptible to overfitting and local minima throughout the training phase. To get around these problems, the weight and threshold parameters of the BP network were improved with PSO, which made convergence and global optimization better.. This hybridization enhanced the accuracy and stability of the model, particularly in datasets with regional heterogeneity.

The PSO–BP model outperformed the classical regression and grey prediction models in terms of prediction accuracy, as shown by the experimental results. Also, the optimized model made prediction errors much smaller across different groups of people, which showed that it could generalize better. Adding PSO to the BP network made it better at learning from complicated economic data. This was accomplished through the integration of global and local search methodologies, resulting in enhanced performance across various evaluation metrics, including accuracy, recall, and F1-score.
This study contributes to the existing literature by illustrating that the predictive capabilities of income estimation systems can be improved through the integration of metaheuristic optimization methods with neural networks. The results support the adoption of hybrid models that integrate the parameter-tuning efficiency of PSO with the nonlinear feature extraction capabilities of BP to generate more precise and sustainable income forecasts. The roadmap of the process is visualized in \ref{fig:5}.


\begin{figure}[!ht]
	\centering
	\includegraphics[width=0.95\textwidth]{figures/fig5.png}
	\caption{Construction Process of BP-PSO Neural Network Model}
	\label{fig:5}
\end{figure}
\subsubsection{Credit Risk Analysis Factors}
One major component of this project is determining the features that affect the credibility of a potential lead or existing customer. A study conducted by Victor Chang, Qianwen Ariel Xu, Shola Habib Akinloye, Vladlena Benson, Karl Hall from 2024 \cite{chang2024credit}, aims to predict the major features to assess credibility based on data analysis from a large random sample. The features that they found to be considerably important can be listed as:
\begin{itemize}
	\item \textbf{Credit Limit:} The credit limit provided to a client based on national regulations.
	\item \textbf{Demographic Data:} Factors such as age, educational level and marital status play an important role in credibility. The age distribution, the mean were calculated during the data analysis. The gender in this case plays a nearly redundant role but is still to be considered.
	\item \textbf{Repayment History:} Especially the short term repayment history is perhaps one of the most crucial indicators of the credibility of a customer. The information includes whether there is any consumption of loan, status of  late repayments and the payment terms.
	\item \textbf{Bill Amount \& Payment Amount:} TThe total amount stated on a customer’s credit card status over a time period and the payment status of the credit card loans.
\end{itemize}

\subsubsection{Practice Principles Derived from Corporate Reports}
The conclusion stating no material misstatement and consistency with the management report \cite{BDDK2022} grounds how we treat finance-based inputs in this study: financial scales and ratios are cited explicitly as “interim/solo” sources; results are presented with sensitivity/robustness bands; and balance sheet–income statement–cash flow integrity is maintained through versioned data lineage and a controlled metadata dictionary. The same finding frames threshold design as a quantitative alignment task with the institution’s risk appetite–limits–early-warning–exception architecture: FP/FN costs are mapped to policy metrics; override cases carry recorded rationale and approvals; and scenario/stress exercises preserve statement-level consistency throughout.

The internal control \& risk arrangement and the explicit split between model development and validation \cite{HayatFinans2024Faaliyet} set the operating baseline for the model lifecycle: independent validation, periodic recalibration, and formal stage gates. Emphasis on IT audit, logging, and centralized monitoring translates into production-grade observability (alarms, SLAs, latency), drift tracking (data/feature/output), rollback, and staged rollout (A/B, canary) as default MLOps practices. The report’s focus on data analytics and AI anchors explainability (e.g., SHAP contribution analysis), access/change control and a complete audit trail, with documentation aligned to BDDK compliance expectations.


\subsection{Data Analysis of National Statistics}
To ground our approach, we found it helpful to examine national statistics on income distribution and household finances in Türkiye.The necessity for more comprehensive modeling is underscored by these insights from the Turkish Statistical Institute (TÜİK) and the Confederation of Turkish Trade Unions (TÜRK-İŞ), which provide context for typical income levels, inequality, and the cost of living. National statistics published by the Turkish Statistical Institute (TÜİK) and the Confederation of Turkish Trade Unions (TÜRK-İŞ) provide a conceptual framework for understanding income distribution and household financial capacity in Türkiye. According to TÜİK's Income and Living Conditions Survey, household income is not limited to official wages; it also includes self-employment earnings, rental income, investment returns, and social transfers. This comprehensive definition emphasizes that a household's true economic capacity extends beyond the salary data currently obtained through the E-Government system, which serves as the sole source of income verification at Hayat Finans. Furthermore, TÜİK utilizes the modified OECD equivalence scale to recalculate "equivalent household disposable income" by adjusting incomes for household size and composition.  This measurement serves as the basis for determining inequality measures, such as the P80/P20 ratio and the Gini coefficient, which range from 0 to 1.
To prevent this handicap, we will also add a location-based segmentation feature that is critical. For this feature, the data published by TÜİK (Turkish Statistical Institute) on the distribution of household consumption expenditure by type, Level 2 of the Istanbul Metropolitan Municipality (BBS), 2024, and the distribution of consumption expenditure by type in 20\% groups ranked by expenditure, Turkey, 2002-2024, are of critical importance. At this point, the neighborhood-based average rent data published by Emlak360 will also be crucial.

National studies conducted by TÜRK-İŞ (Turkish Statistical Institute) establish the concepts of hunger and poverty thresholds, which reflect the minimum income required for individuals and families to maintain basic living conditions. For example, according to TÜRK-İŞ's September 2024 report, the hunger threshold for a family of four is 19,830.73 TL per month, while the poverty line, which covers food, housing, clothing, transportation, and other basic needs, has reached 64,595.21 TL (TÜRKİŞ, 2025).These thresholds show that official wage records often don't show the amount of money needed to live a decent life. The sectoral credit ceiling (LCC) is a set of rules that banks in the banking industry must follow. It lets banks set loan limits based on a certain multiple of verifiable income after essential living costs are taken into consideration. Because of this, people who have other income sources that aren't documented may seem poorer than they really are when only looking at E-Government wage data.
The fact that official and actual revenue are structured differently shows how limited the single-source verification method is in digital banking. It shows how important it is to use a broader, data-driven estimation model that considers information from national statistics and other income indicators. Adding these ideas to Hayat Finans's process for figuring out how much money someone makes will help the bank make more complete and accurate credit decisions that match the real financial situations of its customers.


\subsection{Initial Data Analysis}
The database pertinent to bank loan applications exemplifies a compilation of client information preserved for a specified duration in Excel format. Each row or view contains data regarding the records of services rendered to customers, encompassing customer education, age, debt, and other pertinent factors across 100 characteristics.To gain an initial instance about the data, we have conducted a correlation analysis of all features with respect to the net income provided by the E-Government integration. In many cases observed in literature review, the multicollinearity among features give an idea about what features are likely to be listed as the most relevant outcome from the machine learning approach. This approach also helped us analyze how consistent the numerical values in the data were. For example, intuitionally, we expect a strong correlation between gross and net income values. The correlation results for the initial dataset is shown in \ref{fig:6}:

\begin{figure}[!ht]
	\centering
	\includegraphics[width=0.95\textwidth]{figures/fig6.png}
	\caption{Correlation Heatmap (Top 20 Features)}
	\label{fig:6}
\end{figure}

We looked at the relationship between demographic factors and model-estimated income (Model\_Gelir) by looking at two main factors: age and level of education. These are often good indicators of how much money someone can make, so they are helpful for judging how well and fairly income prediction methods work. \ref{fig:7} is a scatter plot of age and Model\_Gelir. It shows a positive linear trend with a correlation coefficient of about +0.31. This means that older people usually get more money based on models. The trend line in the scatter plot backs up this one-way link even further. This is in line with what was expected, since income usually goes up with age because of career advancement and gaining more experience on the job.

\begin{figure}[!ht]
	\centering
	\includegraphics[width=0.95\textwidth]{figures/fig7.png}
	\caption{Relationship Between Age and Predicted Income}
	\label{fig:7}
\end{figure}

\begin{figure}[!ht]
	\centering
	\includegraphics[width=0.95\textwidth]{figures/fig8.png}
	\caption{Distribution of Predicted Income Across Education Levels}
	\label{fig:8}
\end{figure}

Here \ref{fig:8} illustrates how the predicted income is spread out across different levels of education. There is a boxplot of Model\_Gelir in \ref{fig:8} that shows how it changes with different levels of education. There are numbers that show education, with higher codes meaning higher levels of education (for example, 1 = Primary, 6 = Bachelor, 7 = Postgraduate). The data shows that people with more education tend to make more money and have a wider range of values at the top. The correlation between educational attainment and model-estimated income is about +0.29, which means there is a positive link. This supports the notion that the income model reflects fundamental socio-economic trends: individuals with higher education typically possess greater earning potential, as indicated by the model's forecasts.

\begin{table}[!ht]
	\centering
	\caption{List of variables and their descriptions used in the income prediction model.}
	\label{tab:table1}
	\begin{tabular}{clp{9cm}}
		\toprule
		\textbf{No} & \textbf{Variable Name} & \textbf{Description} \\
		\midrule
		1 & Edevlet\_Net\_Gelir & Net income that was officially reported and taken from e-Government data. \\
		2 & Edevlet\_Brut\_Gelir & The gross income value from e-Government data before taxes and deductions. \\
		3 & Final\_Gelir & The final income value used to decide whether or not to give a loan after making changes within the company. \\
		4 & Final\_Limit & The customer's last approved credit limit. \\
		5 & Edevlet\_Skoru & The e-Government financial score calculated based on the declared income and financial behavior. \\
		6 & HF\_Odeyebilecegi\_Taksit\_Tutari & The maximum monthly installment amount that the customer can afford. \\
		7 & HF\_APP\_INST\_AMT & The actual monthly installment amount of the customer’s current loan application. \\
		8 & HF\_APPLICANT\_EMPLOYMENT\_STATUS & Employment status of the applicant (e.g., employed, self-employed, retired). \\
		9 & Basvuru\_Skoru & Application score assigned by the credit decision model. \\
		10 & HF\_EDUCATION & Education level of the customer (numerically coded). \\
		11 & KKB\_SCORE & Credit bureau (KKB) score representing the customer’s overall creditworthiness. \\
		12 & Talep\_Limit & Requested credit limit in the loan or card application. \\
		13 & PMML & Internal model code used for machine learning model deployment. \\
		14 & HF\_CUST\_WORK\_TITLE & The customer's job title or job. \\
		15 & HF\_ASSET\_PRTCPATION\_ACC\_L3M & The average balance of your participation account (interest-free savings) during the last three months. \\
		16 & KKB\_MORTG\_TOT\_LIM\_AMT & The credit bureau documents show the whole mortgage limit. \\
		17 & Ihtiyac\_modeli\_Final\_skor & Final score generated by the personal loan model. \\
		18 & VARLIKLI\_FLG & A binary flag that shows if the customer is considered rich. \\
		19 & HF\_TOT\_GPL\_MONTHLY\_INST\_AMT & The total amount of monthly payments for all loans that can be used for anything. \\
		20 & KKB\_CC\_OPEN\_MAX\_LIM\_AMT & The highest limit on all of your open credit cards right now. \\
		\bottomrule
	\end{tabular}
\end{table}


\section{Proposed Solution Strategy}

Based on our research and the analysis of current gaps, we suggest a solution strategy that focuses on a dynamic, machine learning-based revenue forecast system. This system will combine data from many sources, use powerful algorithms, and keep learning from fresh data. The strategy includes a high-level project plan, assumptions, limits, goals, an approach, and projected results.

\subsection{Critical Assumptions}

\begin{itemize}
	\item \textbf{Income-Driven Credit Capacity:} We assume that the maximum credit limit an individual can be offered is fundamentally constrained by their income. In practice, lenders enforce this through regulations or internal policy. For instance, in Turkey the banking regulator (BDDK) caps a consumer’s total credit card limits at twice their monthly income in the first year, and four times their monthly income thereafterunder that amended law under Regulations onCredit Card and Debit Cards \cite{BDDK2022}. This means a customer’s credit capacity (the upper bound of credit limit) is proportional to verified income. We take this as a constraint in our model.
	\item \textbf{Creditworthiness Outside Scope:} We assume all customers under consideration meet the basic creditworthiness criteria (acceptable credit scores, payment history, etc.), and credit scoring is handled separately. In other words, our focus is on how much credit to extend based on income, not on whether to extend credit at all – analogous to how routing was out-of-scope in the example model. We treat the customer pool as given and creditworthy; the probability of default or credit score is not explicitly modeled in this optimization (other than through constraints and assumptions about safe debt-to-income levels). This lets us concentrate on optimizing credit volume under income constraints.
	\item \textbf{Customer Segmentation for Operations:} We were let know by our industrial advisor that customers are divided into two segments within the bank: those with deposits above and below 1,500,000 million. We assume this segmentation is correct and necessary. However, since we need to create three new customer segments: low-income, middle-income, and high-income, we assume this segmentation needs to be added after implementing it in the model database, without destroying the existing segmentation.
	\item \textbf{Residential location is related to income:}When creating the model, district-based scoring of customers was performed assuming a correlation between the total income of the district and neighborhood they reside in, regardless of whether they are renters or homeowners. It was assumed that neighborhood rents, along with the individuals' additional income, would provide an indication of their total income. Rents were assumed to be significantly determined not only by location and location but also by income level. Total income was assumed to vary in line with neighborhood rents, meaning that higher rents were assumed to indicate higher total income.
\end{itemize}

\subsection{Major Constraints:}

Implementation and optimization of credit limit strategies within Hayat Finans are subject to a predetermined set of practical, institutional, and regulatory constraints. These constraints guarantee that all model outputs and policy decisions are operationally feasible within the bank's internal capacity, aligned with risk management principles, and compliant with legal requirements. In addition to regulatory limits, such as income-based credit caps and debt-to-income thresholds, the model must also integrate capital adequacy requirements, operational processing limitations, and ethical lending standards that align with both domestic (e.g., KVKK) and international \cite{BIS2011BaselIII} regulations. This section discusses the major constraints affecting model design, parameter limits, and real-world implementation, providing a foundation for risk-aware and sustainable decision-making.

\subsubsection{Company Regulations and  Restrictions}

This section discusses the regulations specific to Hayat Finans explained in our meetings with the company. These are essential to take in consideration when modelling our solution approach.

\begin{itemize}
	\item \textbf{Debt-to-Income Ratio (DEI) Thresholds:} A customer's debt-to-income ratio must consistently fall below a specific threshold, which is typically between 30 and 40\% for consumer credit. This is a requirement of numerous lenders. Typically, DEI is determined by dividing the total monthly debt obligations (including minimum credit card payments and loan payments) by the monthly disposable income. This ratio ensures that any assigned limit results in a manageable payment relative to income, effectively aligning with standard guarantees that minimum payments must fit comfortably within disposable income.
	\item \textbf{Bank's Risk Appetite and Capital Constraints:} A company may have an overall limit on the total amount of credit it is willing to provide, depending on its risk management and capital requirements. Capital adequacy regulations require banks to hold capital against credit risks. Optimization must adhere to a total loan portfolio limit; we cannot grant a maximum limit to every eligible customer; we must have a risk budget in place. There should be constraints so that credit limit increases across all customers, or the sum of all limits, cannot exceed a target (or grow by more than a certain percentage). This acts as a capacity constraint on the total loan volume allocated. It ensures the solution is realistic from a balance sheet perspective and protects against excessive credit expansion that could strain the bank's capital or violate its internal risk policies.
	\item \textbf{Operational Efficiency Constraints:} There are practical limits on how many credit limit adjustments can be processed in a given period. Even if decisions are automated to some extent, credit limit increases require some degree of oversight or customer communication. We assume that the credit department (or algorithmic system) has a limited capacity per period to review and approve limit changes. For example, if we have a team of credit analysts or an automated system that can safely process 1,000 limit increases per week without triggering control issues or customer service restrictions, we also consider this. This prevents the solution from unrealistically increasing everyone's limits simultaneously; changes should be phased in according to resource and monitoring limits.
	\item \textbf{Quality and Regulatory Standards:} For compliance, all credit limit assignments must adhere to responsible lending standards. The "ability to pay" rule and similar regulations require lenders to have up-to-date evidence of a customer's income or ability to repay for credit limit increases. We assume Hayat Finans's bank policy is to increase limits only for customers who provide income updates or whose behavior indicates an increase in capacity. In practice, this means the model may flag customers for an increase, but the application may require an income verification step (which relies on our assumption of accurate income data). Every decision must be justified by ability to pay and demonstrated need (spending patterns) that comply with fairness and safety and soundness regulations.
\end{itemize}

\subsubsection{ Relevant Industry Standards}

\paragraph{Basel Risk Management Principles:}In line with \cite{BIS2011BaselIII} guidelines on credit risk, any increase in credit exposure should be weighed against the borrower’s risk of default. Banks can primarily use income as a proxy for capacity but in practice banks would consider the customer’s credit score, existing debt, and delinquency history. A common industry standard is to maintain a conservative approach to high-risk accounts: for example, accounts with recent late payments or very high utilization might be excluded from proactive limit increases regardless of income, such exclusions are assumed to be handled by pre-processing (filtering out delinquent or very low-score customers). Any model therefore should deal with customers who meet a baseline standard.

\paragraph{Customer Experience and Ethical Considerations:}It’s an industry best practice not to extend credit in a way that can harm the consumer (to avoid “locking ordinary people into poverty with excessive debt,” as even policymakers caution). Therefore, even if income technically allows a high credit line, our strategy will not push limits to a point that might encourage reckless borrowing.We incorporate a safety margin below the absolute income-cap if needed, especially for new-to-credit individuals. For instance, a new customer might not immediately get the full limit which is twice the income in the first month. These are soft constraints governed by policy (e.g., “no customer’s limit should double in less than 6 months to avoid sudden borrowing capacity jump”), which we can include as needed.

\paragraph{Accounting for Inflation and Economic Trends:}Economic volatility, industry practice is to incorporate macroeconomic scenarios into planning. If high inflation is expected to continue, nominal incomes will rise (which raises caps) but also cost of living rises (which could tighten effective disposable income). Any model should be run under different inflation scenarios to ensure robustness. Simulations should be done for the model to ensure given recommendations still hold under different scenarios, scenario analysis is in line with stress testing standards banks use (though typically for loan defaults). Credit limit strategy should “stress test” under adverse conditions (e.g., if a recession hits and incomes drop or spending patterns change). This will provide a broader consideration to ensure solutions remain sound under various economic conditions.

\paragraph{Integration with Credit Lifecycle Management:}In practice, creditworthiness will be part of an ongoing credit management process that includes monitoring (periodic review of usage and payment behavior) and adjustment (not just increases but also decreases if risks arise). Industry standards dictate that if a customer's risk profile deteriorates, the bank may lower the credit limit or take other action. Any proposed limit is subject to ongoing review. The model should be paired with rules that freeze further increases for customers whose credit scores drop significantly or who begin to default on payments. This aligns with standard risk mitigation practice and ensures the safe implementation of the model's outputs.

\paragraph{Data Privacy and KVKK Compliance:}In addition to international standards, banks operating in Türkiye are bound by the Personal Data Protection Law (KVKK – Law No. 6698). This means that any data used for modeling must follow the rules of legality, purpose limitation, and data minimization. The bank only processes information that is directly related to the customer. It can not ask for third-party data, like a spouse's income, because that would mean processing another person's personal data without their permission. So, all variables must come from the customer's own verified sources, like E-Government or Credit Bureau, or from anonymized and aggregated external data. This ensures that credit decisions are made in a manner that follows the law and is fair.

\paragraph{Legal Lending Limits:}According to the regulations of the Banking Regulation and Supervision Agency (BDDK), digital banks are required to comply with income-based restrictions in their credit allocation processes. Under Article 5, Paragraph 4 of the regulation entitled “Restrictions on the Activities of Digital Banks,” the total amount of unsecured consumer loans, excluding credit card expenditures, cash withdrawals, and overdraft accounts that may be extended to a single customer is capped at four times the customer’s average monthly net income, as declared and verified by the bank. If the customer’s income cannot be determined, this limit is set at ten thousand Turkish Lira. Importantly, such income verification applies only to loans provided by the digital bank to its own customers. For this purpose, banks are permitted to utilize income estimation models, which may take into account factors such as repayment history, existing liabilities, credit card limits, and socioeconomic indicators. Nevertheless, these models must be grounded in objective criteria, produce results consistent with supporting documentation, and be maintained in a manner that allows for effective auditing.

\subsection{Objective}
The primary objective of this project is to minimize credit-related risk in Hayat Finans’s lending process by developing a data-driven income estimation model that enables more accurate and fair assessments of customers’ actual financial capacity. Currently, the bank exclusively evaluates credit eligibility using guaranteed but inadequate data sources, which do not account for supplementary income sources such as rental revenue, freelance earnings, or investment returns. As a result of this limitation, customers’ financial capacity is often underestimated, leading to the denial of credit to individuals who are actually able to repay.

By utilizing advanced data analytics and machine learning techniques, the project aims to determine the true disposable income of customers by analyzing a wider range of financial and behavioral indicators. After the model is enhanced, the bank will be able to provide credit to a broader, more precise customer base while simultaneously sticking to responsible lending parameters. Overall, the project aims to improve the efficiency, accuracy, and inclusiveness of Hayat Finans's digital credit evaluation procedure.

\subsection{Solution Approach}
The first step of the solution is to prepare a dynamic model that predicts the income of a customer using the feature importances determined from historical data. To achieve this, we used a machine learning approach defining the problem as a supervised binary classification problem in the domain of machine learning.The problem requires a supervised machine learning approach since the target variable, which is the outcome, is known to be the income. Supervised learning deals with two types of problems: classification and regression. The income prediction problem is determined as a  regression problem since it aims to predict a continuous income value in contrast to a binary classification.

This model aims to give a prediction based on testing relevant machine learning algorithms and choosing the best fit. After this step, the model gives an important multiplicator to be used as weights when predicting the income.

The second step of the problem is to analyze the correlation between location and income. Based on data provided by TÜİK (Turkish Statistical Institute), we determined the average income for each neighborhood. We created a scatter plot that considers the model's output and the estimated income of the customer's neighborhood for each district. We used linear regression to find the best-fit line and its slope. This provided a threshold value for each district using a location multiplier, and by mapping a new attribute to customer characteristics, we added the effect of district-based location to the revenue estimates. We used regression analysis and MSE to find the error in our estimates based on e-Government net revenue data.


\subsubsection{Rationale for Machine Learning Application}
The adoption of machine learning methodologies for this income prediction problem is justified by several compelling factors. Firstly, we want the income prediction method to be dynamic, self-adjusting and data-driven. Machine learning helps identify patterns with relations between features. Identifying the relationship of other features to the actual income value helps determine thresholds for features and get a numerical outcome signifying the importance of features for predictions. We are dealing with a large-scale dataset of features for various customers. Traditional approaches lack in making efficient and detailed data analysis so it is essential to use a more automated approach like machine learning. This large scale also leads to the intuitional assumption that the features will have a complex non-linear relationship with income. Our model uses various complex machine learning algorithms to identify these relationships from different algorithms’ aspects and provide a numerical income accuracy since the nature of machine learning is rule-based, multi dimensional and precise.

\subsubsection{ Methodological Approach and Algorithm Selection}

The model uses a machine learning approach to provide a dynamic and measurably accurate income prediction as well as providing a threshold for features based on importance in prediction to be used in future predictions for new customer arrivals or data changes.

\subsubsection*{ Data Preprocessing Pipeline}
The implemented solution follows a rigorous preprocessing protocol:

\begin{itemize}
	\item \textbf{Missing Value Treatment \& Feature Verification:} When we analyzed the customer sample data provided by the company, we realized that some values were missing for some customers. For instance, for customers who left the system without the E-government integration, the features retrieved from that system were missing. Another missing value we found frequently in the system was, for customers who never had to take a loan or did not own a credit card, some KKB values were empty or insignificant. To deal with these issues, we integrated an automated package that treats the missing values in the data accordingly to ensure integrity. Another problem we had to deal with was that some of the features in the data were irrelevant for our estimation. We had to be removed manually from the Excel file before the implementation to our model. The second part is still in progress as we need to learn the meaning of each of the features and how they are retrieved.
	\item \textbf{Feature Scaling:} A standardization process for algorithms was applied to numerical features for algorithms sensitive to feature magnitudes. This was handled using Python packages that have this process embedded as a function.
	\item \textbf{Stratified Data Partitioning:} An 80-20 train-test split with stratification preserves class distribution across subsets. The test-split ratio was determined from literature review and some frequent basic machine learning approaches.
\end{itemize}

\subsubsection*{Exploratory Data Analysis}
Raw data, or unprocessed data, are only beneficial if there is potential value in analyzing it. Exploratory Data Analysis (EDA) entails the examination and visual depiction of data to extract insights from it. Exploratory Data Analysis (EDA) entails the examination and visual representation of data to extract insights, including the summarization of key data characteristics to enhance comprehension of the dataset \cite{chang2024credit}. In preparation for model development, an exploratory correlation analysis will be conducted on the available data. The goal is to identify which features have strong relationships with income, thereby validating intuitions from the literature and national statistics with empirical evidence. Features such as education level, occupation group, and self-reported income have the strongest correlation with the target (predicted income), whereas SGK-verified income levels are off at the cap.

\begin{figure}[!ht]
	\centering
	\includegraphics[width=0.95\textwidth]{figures/fig9.png}
	\caption{Declared vs Model-Predicted Income Graph (Between 0-200,000 TL)}
	\label{fig:9}
\end{figure}

A scatter of predicted vs. declared income reveals close alignment for mid-range incomes but systematic underprediction at the top end due to SGK capping.This scatter plot compares customers’ self-declared income (x-axis) to their income predicted by our ML model (y-axis). The red dashed line indicates perfect agreement (y = x). The orange regression line shows a systematic underreporting trend—model-predicted income frequently exceeds declared values, especially in the TL 20,000–100,000 range. This supports the hypothesis that many Tier-4 applicants underdeclare income or possess unreported earnings (e.g., rental, freelance), making the SGK 4A-only verification inadequate.

In Türkiye, official SGK 4A contributions report salaries only up to a statutory ceiling. As of January 2025, the maximum amount of money that may be insured by SGK is 195,041.40 TL per month \cite{pwcWorldwideSummaries}. This means that any actual wage beyond this ceiling will be cut off at the cap in SGK records, which will make it look like high-income borrowers are not being counted. In reality, a lot of high earners or flexible workers are bound to make inaccurate claims about their income to pay less in taxes and contributions. This means that SGK data alone doesn't show how much money they really make. So, if you only use SGK 4A, the model will be less likely to find big earners.

This scatter plot shows that depending just on SGK 4A for income verification has an obvious systemic issue. Because SGK contributions are limited to about 195,000 TL/month \cite{pwcWorldwideSummaries}, those with high incomes are cut off at this level, which leads to underreported income. The orange line shows that our model's projections are always higher than the SGK-declared numbers for the highest income levels. This supports the idea that many applicants are unfairly excluded or given lower credit limits because their income is underestimated.

\begin{figure}[!ht]
	\centering
	\includegraphics[width=0.95\textwidth]{figures/fig10.png}
	\caption{KKB Score vs Verified Net Income Graph}
	\label{fig:10}
\end{figure}

\ref{fig:10} examines the relationship between Findeks (KKB) credit scores and verified income obtained via e-Government SGK 4A records. Similarly, credit score (Findeks/KKB) and credit utilization (revolving balance/limit) correlate with income: lower utilization and higher scores generally align with higher incomes. High-credit-score customers tend to cluster at moderately higher income levels. For customers without accessible SGK records, especially in Tier-4, the credit score may act as a soft indicator within a broader income estimation model though it should not be used as a sole proxy due to low explanatory power.

\subsubsection*{Ensemble Algorithm Evaluation}

This section of the solution generation was generated using the ideas and cases mentioned in the literature review. The methodology employs a comparative analysis framework, systematically evaluating seven distinct machine learning algorithms:

\begin{enumerate}
	\item \textbf{Logistic Regression:} Logistic regression gives its outcome providing a sigmoid function of different classes and finds the best linear combination to separate the data. It is a relevant approach in the banking industry and provides a probability-like statement about the objective. It also provides a very fast and easily interpretable outcome.
	\item \textbf{Decision Tree:} Uses a binary(yes/no) approach to come to a mathematically significant conclusion. One hot encoding is used to interpret the data as a binary table. Data is split with respect to feature values. Decision trees provide a feature importance ranking with a very similar approach to human decision making process but it is faster and more robust.
	\item \textbf{Random Forest:} The working principle is very similar to decision tree algorithm but it creates many trees hence provides a more accurate scalar for feature importance.
	\item \textbf{Gradient Boosting:} It is again a tree-based approach. Gradient Boosting also creates more than one tree but the tree creation gives focus to the misinterpreted data in the previous trees. Gradient Boosting is usually the most accurate algorithm which was observed from literature review but can also be interpreted from the intuitionally estimated accuracy based on the working principle.
	\item \textbf{Support Vector Machine(SVM):} Separates the data by finding optimal boundary hyperplanes to separate different classes with maximum margin. It can handle non-linear patterns and identify complex relations but it is relatively harder to explain.
	\item \textbf{K-Nearest Neighbors(KNN):} It creates classifications by analyzing similarities between datapoints and looks at the K most similar datapoints to provide an outcome. Although it is a relatively slow process, it is very useful to identify outliers or unusual datapoints.
	\item \textbf{Naive Bayes:} Naive Bayes is  a probabilistic classifier that uses Bayes' theorem which assumes features are independent to provide a probabilistic outcome. It is a very fast working algorithm that provides a solid base to make comments on the data.
\end{enumerate}


\subsubsection*{ Model Selection Criterion}

The model tries to make predictions on the data using each algorithm and each algorithm comes up with its own outcome. Those outcomes are analyzed using the following error quantifications:

\begin{itemize}
	\item \textbf{Test set accuracy:} The accuracy point of the prediction made from 80\% of the data is then tested by using the weights of features to predict the income on the remaining 20\% and comparing the accuracy. This can be perceived as the primary performance indicator measuring prediction correctness on held-out data.
	\item \textbf{Cross-validation score:}: Cross-validation provides robust performance estimation. It is also useful to mitigate the overfitting risks.
	\item \textbf{R\textsuperscript{2} Score:} The R\textsuperscript{2} value was a key decision metric to decide the optimal model since it indicates the proportion of the variance explained.
	\item \textbf{RMSE, MAPE, MAE:} The Root means square error, mean absolute percentage value, means average error were also calculated for each model for further insight on prediction accuracies.
	$\text{MAE} = \frac{1}{n}\sum |Y_i - \hat{Y}_i| \quad \text{and} \quad \text{MSE} = \frac{1}{n}\sum (Y_i - \hat{Y}_i)^2$ quantify average error magnitudes.
\end{itemize}

The model achieving the highest test accuracy is designated as the optimal classifier, with cross-validation and R\textsuperscript{2} scores serving as a reliability indicator.

\subsubsection{Feature Importance Quantification}
After determining the optimal algorithm for income predictions given the current state of data, the model uses the assigned algorithm to quantify the importance of each feature for future estimations. This is an essential part to make future estimations.


\subsubsection*{Importance Weights as Multiplicative Denominators}
Tree-based models calculate feature importance through mean decrease in impurity (Gini importance). Each feature receives an importance score representing its contribution to predictive accuracy across all decision trees. These scores are normalized, summing to unity. This score is essential for our model as it will provide the multiplicators for each feature when making future estimation of income. The importance threshold with given data is shown in \ref{fig:11}. It is important to take in consideration that some of these features will be redundant after the data processing is complete:


\begin{figure}[!ht]
	\centering
	\includegraphics[width=0.95\textwidth]{figures/fig11.png}
	\caption{Top 20 Most Important Features and Their Percentages}
	\label{fig:11}
\end{figure}

The prediction process utilizing feature importance can be conceptualized as:
For each instance \textbf{x} with features $\{x_1, x_2, \ldots, x_n\}$ and corresponding importance weights $\{\;w_1, w_2, \ldots, w_n\}: \quad \hat{y} = f(w_1 x_1,\, w_2 x_2,\, \ldots,\, w_n x_n)$

This estimation provides an outcome by assigning a weight positively correlated with the observed effect of the feature on the income based on calculation conducted on the historical data.

\subsubsection{Model Evaluation and Validation}
An additional feature that we added to the model to visualize the outcomes and make an analysis on the model’s accuracy. The outcomes with the initial data have been explained and shown throughout the report. Apart from conclusions derived from visual report outputs, we used different approaches for the validation of the model.

\begin{itemize}
	\item \textbf{Confusion matrix analysis:} Quantifying true positives, true negatives, false positives, and false negatives. The false positives and true negatives are type1 and type2 errors respectively. They provide an indicator that the model lacks accuracy.
	\item \textbf{Machine algorithm metrics:} Precision, recall, F1-score for each class were calculated for further validation to ensure the results for different measures are in compliance.
	\item \textbf{Cross-validation statistics:} Cross validation gives mean accuracy and standard deviation across folds as output for validation. It is again a benchmark approach in machine learning.
\end{itemize}

\subsubsection{Adding District-Based Location Score Feature}

Since  HayatFinans is a digital bank, estimating customers' total income, including not only their salaries but also their additional income, has become a critical challenge. Since one of the key indicators of this is residence, and a province-based estimate would be insufficient, the company deemed district-based location mapping crucial. The mapping strategy we developed to achieve this is as follows:

\begin{enumerate}
	\item \textbf{Finding a District-Based Location Coefficient:} The average neighborhood rents data taken from \cite{emlak360} will be combined with the average rents of all neighborhoods in that district. The average rent for that neighborhood will be divided into five segments based on whether the average rent for that neighbourhood is lower or higher than the provincial average rent. The average income for that neighborhood will then be calculated by multiplying the income-to-rent expense ratio for each of the five segments extracted from TÜİK (Turkish Statistical Institute) in those provinces. This will be done for all neighborhoods in that district. A scatter plot that provides a relationship between the estimated income based on location and estimated income from the model will be formed. The initial indicator for the location-based coefficient will be done by finding a best fitting line using linear regression. The error of this estimation can be found using regression analysis.
	
	\item \textbf{District-based location coefficient segmentation:} The district-based location coefficient will be classified between certain threshold values, and the districts will be segmented into five segments.
	\begin{enumerate}
		\item \textbf{Segment 1 (Very Low Income District)}
		\item \textbf{Segment 2 (Low)}
		\item \textbf{Segment 3 (Moderate)}
		\item \textbf{Segment 4 (High)}
		\item \textbf{Segment 5 (Very High Income District)}
		This segmentation allows the model to account for socio-spatial disparities in purchasing power and income declaration behavior.
	\end{enumerate}
	\item \textbf{Database mapping:} Similar to the mapping for marital status, a new mapping will be created for the district-based score feature, with the customer's score being 1, 2, 3, 4, or 5 based on their district of residence \textbf{district-based location score} $L_i$$\in${1,2,3,4,5}, based on the segment classification of their declared residential address.
	\item \textbf{Adding a district-based location score to the model:} A new feature, the district-based location segment, will be added to the data entered into the model, now segmented and mapped, and added as input to the model. The model will then generate a new feature score for this feature like it does for every other feature.
\end{enumerate}

\subsection{Outcome and Deliverables}

\paragraph{Outcome:}A model that calculates the prediction accuracy for each relevant machine learning algorithm along with hyperparameter tuning will be prepared. The model will choose the most accurate algorithm based on each algorithm’s fit for a small percentage of the data which will be utilized as a testing batch. It will determine which features have the most significant impact on the income predictions. The model will give the importance level for the features that will be utilized to do the predictions for future user information. Furthermore, the model will take in consideration the effect of location on the income to provide a more accurate prediction as well as limit the necessity for E-Goverment integration for customers.

\paragraph{Deliverables:}The primary deliverable of this project is a dynamic income prediction model which uses tests and chooses the best algorithm for the data with an additional feature to also take in consideration which province the customer is located in, increasing prediction accuracy while minimizing the risk and necessity for processes that create frictions in the customer journey . The project outcome system will dynamically update itself based on updated customer information and provide the best fit for the current system. With this system Hayat Finans will be able to do a dynamic, automated and accurate income prediction by using the data on its customer database.

\paragraph{Benefits to the Company:}The income prediction system aims to provide an income analysis by analyzing existing features with the addition of the location feature based on customer information for better customer service quality, more accurate fraud detection, increased expected revenue from loans and most importantly minimized risk in income prediction. This machine learning approach makes income prediction with a broader feature span that allows flexible decision making as well as analyzing the impact of new features, in our case location. This prediction system decreases the necessity for additional steps in the current system that aim to make sure data on features is reliable while still providing a highly accurate income prediction. The system also decreases the chances of fraud by providing a more detailed customer analysis. Overall, the project will allow Hayat Finans to make more accurate and beneficial financial decisions providing a reliable customer base and a tailored customer experience.

\subsection{Implementation of the Model to the Current}

Hayat Finans currently uses PowerCurve to visualize the income prediction thresholds and make the predictions. Our model uses a more detailed, dynamic prediction system incorporating machine learning expertise. Therefore, we are aiming to design a user-friendly interface that retrieves the customer data, reruns the prediction model including the location data and delivers an output for the estimated income and whether the data is reliable to proceed with further tasks. The interface is going to be implemented using a GUI in compliance with Python.

\subsection{Project Plan and Work Package Assignments}

On October 10, 2025, the project members, industry advisors, course coordinator, and academic advisor gathered for the kick-off meeting. Alperen Doğan, the industry advisor, introduced the company and provided us with an overview of the project's targets and expectations. Towards the end of the meeting, the necessary data was requested from the company.

Individuals responsible for specific tasks were identified and appointed on the project management page. Subsequently, the first stage, focusing on the proposal, commenced. Nazlıcan Akman, the group's contact person, scheduled meetings, which were conducted with different combinations of group members, initiating literature research. Later, a collective meeting was organized for the proposal draft. During this session, the objective function was determined, the work was divided, and a report was prepared collaboratively.


\subsubsection{Work Package 1: Analysis of the Current Problem}

Analyzing the problem allows one to see the current issue in order to comprehend the issue in its existing form. Meetings with the academic and industry advisors were held. These sessions led to the conclusion that  the income prediction using E-Government data caused significant potential customer loss but it was the most risk-free way to predict customers income. So our main objective is to create a reliable income prediction model that minimizes the necessity for the mentioned process. A useful implementation of features that we foresee will increase the accuracy of prediction is the location effect on the estimation. Every member of the team contributed to this task bundle. Nonetheless, the primary responsibility for overseeing the procedure and setting up the meetings fell to Nazlıcan Akman. The explanatory analysis and the sorting of the current data that was received was made by Selvi Ecem Civelek and Arda Noyan Karaşoğlu. It was proposed by Arda Noyan Karaşoğlu to forecast data with machine learning models as a result of the analysis of the problem. Assumptions, constraints, and the objective function, which are the keys to the problem solution, were determined by Göze Yılmaz and Şima Ünver, which then was confirmed by the group. The location based approach was generated by Nazlıcan Akman and Selvi Ecem Civelek along with the industrial advisor. This work package is considered mostly done.

\subsubsection{Work Package 2: Literature Review and Modelling}

All machine learning algorithms relevant to income prediction models were researched. Literature reviews that can be used for classification algorithms, hyperparameter tuning and forecasting income were carried out. Although this process was mostly carried out by Arda Noyan Karaşoğlu and Selvi Ecem Civelek, everyone in the group was actively involved. Then, the implementation of the machine learning algorithms to test the best fit to the model will be done by Selvi Ecem Civelek. The LP modeling was brainstormed with the whole group with the leadership of Nazlıcan Akman and Arda Noyan Karaşoğlu. The location based literature review was mostly conducted by Göze Yılmaz, Nazlıcan Akman, Orkun Arslan and Şima Ünver. The modelling technique was researched and generated by Nazlıcan Akman and Selvi Ecem Civelek. This work package is still in progress because of the absence of the mass data.

\subsubsection{Work Package 3: Verification Process}

The verification process is a method used to confirm the accuracy and authenticity of information, forecasts, claims, or documents. The work for this package and the following packages has not started yet. However, the plan we have in mind includes a regression analysis and MSE but the brainstorming process is still ongoing, is open to changes, and will be divided into sub-branches later on. Selvi Ecem Civelek, Göze Yılmaz and Şima Ünver are planned to be in charge of the control and progress of the process.

\subsubsection{Work Package 4: Validation Process}

A method of quality control and assurance called validation is used to make sure that a system, product, process, or service satisfies predetermined standards and requirements. Validation is different from verification in that the former is mostly concerned with confirming that the system or product has been constructed correctly, while the latter is concerned with ascertaining whether it satisfies the intended needs and user requirements. Orkun Arslan is the main responsible member of this stage.

\subsubsection{Work Package 5: Pilot Study}

It is aimed to present the work we have done to the Hayat Finans team and regulate the test of modeling made on a small group in order to control the accuracy. The person responsible for this process is Selvi Ecem Civelek and Nazlıcan Akman.

\subsubsection{Work Package 6: User Interface Prototype}

In this interface section, a user-friendly study will be carried out in which new data will be forecasted based on past data and implemented features provided by Hayat Finans and our newly embedded location feature. Selvi Ecem Civelek, Arda Noyan Karaşoğlu and Orkun Arslan are responsible for carrying out the process.

\subsection{Gantt Chart}
The planned operation of this process can be easily observed in the Gantt
chart in Figure

\begin{figure}[!ht]
	\centering
	\includegraphics[width=0.95\textwidth]{figures/fig12.png}
	\caption{Gantt Chart of the Project}
	\label{fig:12}
\end{figure}

\end{document}