CREDIT RISK ANALYSIS OF LARGE SCALE FIRMS BASED ON FINANCIAL DATA

Application Track:

Code:

Domain:

Proposed by:

Entity Logo:

Summary of the entity:

Yapı Kredi Teknoloji is a Turkish company that delivers outputs in machine learning, data mining, pattern recognition, artificial intelligence and natural language processing fields and develops mobile applications.

Description of the global challenge:

Non-Performing Loan Detection (NPL) is one of mostly studied problem in commercial banking. At the beginning of each financial year/term, the banks specify their risk appetite in terms of what is the maximum tolerable amount of undischarged debts in a specific time frame. During each term, it is aimed to be collected all late payments from default credits. 

According to the Banking Regulation and Supervision Agency of Turkey, rates of commercial loan applications are highly increasing which makes detecting NPL a more crucial safety measure for the banks. 

Being one of the top banks in Turkey, YKT has access to a lot of important data such as data in the loan applications, demographic data of the clients, financial documents of the applicant companies, credit bureau data related to the company, payment behaviour of the client, among others. 

Before any delinquency occur in one of the scheduled payments of any products, YKT aims to identify the customers that they are probable to be default. Combining these with the state-of-the-art machine learning methods and experience in the field, YKT intends to predict NPL before 6 months of period and decrease the ratio of NPL.

Sub-challenges composing this experiment:

This challenge is composed of 2 sub-challenges:

• Impact of Financial Determinants for Large Scale Firms NPL Detection (REACH-2022-READYMADE-YKT_1.1)
• NPL Detection of New Customers for Large Scale Firms (REACH-2022-READYMADE-YKT_1.2)

Expected global results:

This challenge is designed to employ Machine Learning (ML) algorithms on financial data. Especially, feature engineering techniques on time series tabular data is focused on during the development.

NPL Detection of New Customers for Large Scale Firms

Code:

REACH-2022-READYMADE-YKT_1.2

Summary of the sub-challenge:

The goal is to improve YKT model by analyzing the data of new customers (less than 6 months of customer tenure) on the NPL.

Description of the challenge:

Non-Performing Loan Detection (NPL) is one of mostly studied problem in commercial banking. At the beginning of each financial year/term, the banks specify their risk appetite in terms of what is the maximum tolerable amount of undischarged debts in a specific time frame. During each term, it is aimed to be collected all late payments from default credits. 

According to the Banking Regulation and Supervision Agency of Turkey, rates of commercial loan applications are highly increasing which makes detecting NPL a more crucial safety measure for the banks. 

Being one of the top banks in Turkey, YKT has access to a lot of important data such as data in the loan applications, demographic data of the clients, financial documents of the applicant companies, credit bureau data related to the company, payment behaviour of the client, among others.

Also, payment behaviours for new companies (less than 6 months of customer tenure for YKT) may differ, default probabilities for these companies can be also examined separately. Within the global YKT challenge to predict NPL, this specific sub-challenge aims to improve YKT model by analysing the effect of the data of new customers (less than 6 months of customer tenure) on the NPL.

Data to be used:

Large Scale Firms NPL Detection

Expected outcomes:

This challenge is designed to employ Machine Learning (ML) algorithms on financial data. Especially, feature engineering techniques on time series tabular data is focused on during the development.