Artificial intelligence in credit scoring – Sergey Kondratenko

An artificial intelligence-based credit scoring system is an innovative approach to determining a borrower’s ability to repay a loan.

Unlike traditional credit scoring, which relies on static parameters and historical data, AI credit scoring uses machine learning algorithms, explains industry expert Sergey Kondratenko. They are used to analyze a variety of information sources to predict a borrower’s ability to repay a loan. To fully reveal the significance of this technology in the field of modern financial services, you need to understand how AI affects their availability and credit formation.

Sergey Kondratenkois a recognized specialist in a wide range of e-commerce services with experience for many years. Now, Sergey is the owner and leader of a group of companies engaged not only in different segments of e-commerce, but also successfully operating in different jurisdictions, represented on all continents of the world. The main goal is to drive new traffic, create and deliver an online experience that will endear users to the brand, and turn visitors into customers while maximizing overall profitability of the online business.

Sergey Kondratenko: Artificial intelligence in credit assessment

Scoring is a model that applies mathematical calculations and statistical methods to study and analyze extensive data. Using AI, it provides a more accurate and personalized credit score, taking into account many additional factors in real time. This opens up access to financial services for more people.

AI-based credit scoring solutions are based on data such as:

• total income;
• credit history;
• transaction analysis;
• experience;
• user behavior analytics.

Taking into account the above data, credit scoring evaluates how solvent the client is and is ready to repay the debt. There are approximately 1.5 billion people in the world who do not have access to a bank or similar financial institution. They are called “non-banking”. As for the rest, less than half of the banking population is eligible for a credit loan. Sergey Kondratenko notes: in order to expand the capabilities of banks in this direction, effective solutions such as AI-based credit scoring are used. It can be used to improve this process in a variety of ways.

The expert identifies the following methods:

1. Better data analysis. AI is capable of analyzing large amounts of data from various sources. This helps lenders make a more accurate forecast of a borrower’s creditworthiness.

2. Collect data from various sources – social networks, credit bureaus and financial statements.

3. Data preprocessing and cleaning. At this stage, data about borrowers is collected, then cleaned and structured. This includes removing duplicates, filling in missing values, and converting data into an easy-to-analyze format.
4. Application of machine learning algorithms. Machine learning algorithms are used to analyze pre-trained data to identify patterns and trends. These models are capable of analyzing such parameters and factors as income level, credit history, work experience and many others, in order to assess the likelihood of the borrower repaying the loan.
5. Making lending decisions. The system makes a decision on issuing a loan based on the results of data analysis. It can be automated, based on predefined criteria, or require the intervention of a bank employee – this is for more complex cases.
6. Reducing bias.

– The use of artificial intelligence in credit scoring has an important advantage – the ability to reduce bias, notes Sergey Kondratenko. – Machine learning algorithms consider only objective factors when assessing a borrower’s creditworthiness. This helps reduce the influence of characteristics such as race, gender or ethnicity. This approach promotes a fairer and more equitable review of loan applications.

By taking this responsible approach, lenders can ensure that their credit score system is unbiased. The expert emphasizes that this approach to credit scoring can improve the efficiency and fairness of the loan issuance process. This is beneficial for both financial institutions and borrowers.

Sergey Kondratenko: fast decision making and process automation using AI in credit scoring

Efficiency, accuracy and objectivity are the three key principles that guide a lender when using AI to assess a client’s solvency. This technology helps automate many tasks, thereby reducing data processing time and increasing the efficiency of the process.

– One of the ways to speed up the credit scoring process using artificial intelligence is to automate data entry and analysis, says Sergey Kondratenko. – With its help, lenders can quickly assess a borrower’s creditworthiness and make lending decisions in real time. This can be especially useful for online lending platforms that require quick and accurate credit assessments.

The expert names another way to increase the speed of credit scoring using AI – automating the loan application process. Lenders can provide borrowers with instant feedback on their applications. To do this, they use chatbots and other AI-powered tools that help optimize efforts and significantly reduce the time to complete this task.

The impact of artificial intelligence on financial inclusion – Sergey Kondratenko

Financial inclusion refers to the accessibility and availability of affordable financial products, services and tools for all people and communities. This is especially true for those who are traditionally underserved or excluded from the formal financial system. Inclusion aims to ensure that everyone, regardless of income, gender, race, ethnicity, geographic location or social status, has access to financial services. He needs this to manage his money, make payments, save, borrow, invest and participate in the economy.

Sergey Kondratenko emphasizes that AI contributes to expanding access to financial services in various ways. The specialist identifies the following:

• Improving credit scoring with AI algorithms is the ability to analyze more diverse data sources, including non-traditional ones, to assess creditworthiness. By taking into account factors such as payment history, spending patterns, social media activity and employment history, AI can provide a more complete and accurate assessment of a person’s creditworthiness. This allows lenders to make accurate lending decisions and provide loans to those who might otherwise be excluded by traditional credit scoring models.
• Expedited loan processing. AI allows you to automate and simplify the process of reviewing and approving loan applications. Through natural language processing (NLP) and machine learning, AI-powered systems can analyze loan applications, extract relevant information, and verify documents. This significantly reduces the time and effort required for manual processing.
• Reducing bias and discrimination. AI systems can help minimize bias and discrimination in loan approvals. They exclude variables such as gender, race or ethnicity. The expert believes that this contributes to the development of fair lending practices and ensures that loan approval is based on objective criteria. Importantly, this approach increases financial inclusion for marginalized groups.
• Risk assessment and fraud detection. AI algorithms are capable of analyzing huge amounts of data to identify patterns and trends that are associated with loan defaults and fraudulent activities. The ability of an AI system to identify such potential risks improves the accuracy of risk assessment and allows lenders to provide loans to individuals who would be rejected if a traditional approach were used.
• Alternative credit scoring models. For those who have limited or no credit history, alternative credit scoring models could use AI to determine their creditworthiness, according to Sergey Kondratenko. It’s based on factors such as your utility bill payment history, rent, or cell phone usage. The specialist believes that this approach expands access to credit for underserved segments of the population and promotes their involvement in financial life.
• Personalized loan offers. AI algorithms can analyze a person’s individual financial data and behavior to prepare personalized loan offers for them. Taking into account factors such as income, spending patterns and financial goals, AI can offer loan products tailored to a person’s needs and capabilities.

– This personalized approach helps people access loans that suit their needs. This increases the chances of approval and improves overall financial inclusion, comments Sergey Kondratenko.

• Chatbots and virtual assistants, which operate on the basis of AI, can provide support and recommendations. They can answer inquiries, provide information about eligibility criteria and required documents, and assist people in completing applications. Such virtual assistants make the loan application process more accessible and convenient for users. This is especially important for those who have limited access to physical bank branches or face language barriers.

Summarizing the role of AI in the credit scoring system, Sergey Kondratenko notes that this approach allows financial institutions to make more accurate and effective decisions. AI expands lending opportunities for clients and increases access to financial services. And reducing bias in the lending process allows us to serve a wider range of individuals and legal entities.