Financial Scoring

AI-based credit scoring

What is AI-based credit scoring?

 

AI-based credit scoring is a contemporary method for evaluating a borrower’s creditworthiness. In contrast to the conventional approach that hinges on static variables and historical information, AI-based credit scoring harnesses the power of machine learning (ML) algorithms to scrutinize an extensive array of data from various sources. This advanced approach aims to forecast a borrower’s probability of loan repayment. As a result, AI-driven credit scoring offers a comprehensive assessment of credit risk, providing lenders with a precise and multifaceted understanding of a borrower’s financial behavior.

Credit bureaus and lenders employ credit scoring models to analyze the creditworthiness of individuals or businesses and gauge the likelihood of default on credit obligations. These models consider multiple factors such as payment history, credit utilization, credit history, types of credit accounts, and recent inquiries. Each factor is assigned a weight, and a credit score is computed using a formula based on this evaluation.

Credit scores typically range from 300 to 850, with higher scores indicating lower default risk. Lenders utilize these scores to determine loan terms, encompassing interest rates, repayment durations, and loan amounts. A higher credit score can result in more favorable loan terms, while a lower score may lead to less advantageous terms, including higher interest rates and stricter repayment requirements.

 

How do credit risk models add value to business?

In the financial business world, credit scoring models play a crucial role. It adds value to business in the following manner-

Credit Risk Management

Credit Risk Models allow lenders to evaluate the creditworthiness of individuals and organizations and ensure that their exposure to liability is manageable. This allows lenders to assess the level of risk in their loan portfolio.

Regulatory compliance

Today, credit risk models are required by law. Basel III (a set of international banking regulations) requires banks to use such models to meet regulatory requirements, where they are expected to maintain a certain amount of capital based on the credit risk exposure indicated by the credit risk model.

Scenario Analysis

Stress testing can be done through credit score models, which allow lending firms to analyze various scenarios and test the resilience of their loan portfolio. This, in turn, helps manage losses in case of events like recessions and other financial crises.

Credit risk models also allow lending firms to stay competitive, reduce costs, and mitigate risk. As you can see, several credit score models are used. However, some serious downsides must be discussed along with their various upsides.

 

How does AI-based credit scoring work?

 

AI-based credit scoring models rely on machine learning algorithms to assess an individual’s creditworthiness. These algorithms are trained using extensive historical datasets that include information on borrowers’ financial behaviors and loan repayment outcomes. By analyzing this historical data, ML models identify patterns and correlations indicative of a borrower’s ability or likelihood to repay a loan.

 

The credit scores can hugely impact an individual’s financial and personal life. Therefore, several guidelines have been set for building a credit score model. For example, such models should be transparent and unbiased. The data used for model building should be of high quality.

 

Here’s a more detailed breakdown of how AI-based credit scoring works:

Data collection: AI-based credit scoring models gather data from various sources. Traditional credit information, such as payment history, existing debts, and the length of credit history, is a foundational data source. However, what sets AI-based models apart is their ability to incorporate alternative data sources, which provide a more comprehensive view of a borrower’s financial behavior. These alternative sources can include:

Transaction data: Information about an individual’s financial transactions, including income, expenses, and spending habits.

Internet browsing behavior: Insights into online activities, including searches, website visits, and shopping behavior.

Social media activity: Analysis of social media posts, connections, and interactions to understand a person’s lifestyle and social network.

Data processing and feature engineering: Once the data is collected, it undergoes processing and feature engineering. Feature engineering involves selecting relevant features (variables) that the machine learning model will use to make predictions. This step is crucial in building effective predictive models.

Model training: ML algorithms utilize a preprocessed and engineered dataset for training purposes. During training, the model learns to recognize complex patterns and relationships within the data. It identifies which features are most influential in predicting creditworthiness and how they interact.

Prediction: After training, the model can make predictions on new, unseen data. When a loan application is submitted, the model uses the borrower’s information to assess their credit risk and predict the likelihood of repayment. This prediction is based on the patterns and correlations it has learned from the historical data.

Comprehensive assessment: AI-based credit scoring models provide a more comprehensive assessment of credit risk by analyzing a broad range of data sources. Traditional credit scoring methods rely on historical credit data, which may not capture a person’s financial behavior. In contrast, AI-based models consider traditional and alternative data sources, offering a holistic view of an individual’s financial behavior and creditworthiness.

However, addressing fairness, transparency, and data privacy concerns is essential when implementing AI-based credit scoring to ensure accurate and ethical decisions.

RFM analysis

What is RFM and why it is so important for retail business?

RFM is the short form of:

  • Recency: When a customer last purchased from your brand.

  • Frequency: The number of purchases the customer has made from you in total or during a specific timeframe.

  • Monetary value: The amount of money the customer spends in a transaction. 

RFM analysis enables you to target customers with messages that best match their relationship with your brand. For example, you are likely to have more success suggesting big-ticket items to customers who spend frequently and in large amounts. On the other hand, you are more likely to grow the customer value of your relationships with consumers who make purchases frequently, but only in small amounts, by rewarding them for their loyalty or offering referral promotions.

 

Steps of RFM Analysis

The steps below provide a high-level overview of how an RFM Analysis and segmentation is executed.

  • Build RFM Model

In order to build an RFM model, you need to assign a recency score, frequency score and monetary score to each unique customer. The raw data, which can be collected from a customer database from previous transactions, is then compiled in a spreadsheet or database.

  • Divide the Customer Segment

Next, divide the RFM database into tiered groups for each of the three values of the RFM score. Tier designation is based on the greatest to the least. For example, tier one for monetary value is assigned to the high spenders and tier five is assigned to the lowest spenders.

  • Select the Targeted Customer Group(s)

The third step involves the selection of the segmented customer group with high customer value. Organizing the RFM segment, you can begin to assign titles to segments of interest, such as your best customers, biggest spenders, faithful customers and at-risk customers.

  • Craft a Personalized Marketing Strategy

Finally, craft a unique marketing strategy designed for each RFM segment focused on their behavioral patterns. Utilizing the RFM Analysis, marketers are able to effectively communicate their messaging to customers in a way aligned with customer behavior.

 

 

Engage Customers and Drive Sales With RFM and Product Recommendation Models.

 

Providing personalized product recommendations based on past purchases helps increase engagement, especially with customers identified as “need attention” or “about to sleep” by the RFM model. DSE framework allows you to generate a list of recommended products for each customer automatically.

AI consulting

ChatBot assistants

Computer vision

Financial scoring

NLP, LLM and RAG

Miltech

Prompt engineering

Scientific research

Sports betting (iGaming)

Tabular data and time series

We transform your data  to make it serve you best! 

Our core values:

 

Innovation

Excellence                                                                Equity

Customer Centricity

Kindly consult our Publications page (Blog) to get some inspiration by reading about applications of our products and services  or popular use cases.
 
 
 Alternatively, connect on social networks,
 or simply get in touch:
 

Every day, new happy customers

8

Services

40

Users

30+

Conducted researches