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Data Enrichment

Discover how data enrichment transforms credit risk management by integrating traditional financial data with alternative sources.

Table of contents

In recent years, the USA has observed a trend towards an increase in the number of overdue payments on consumer loans. 

If in 2021, there were only 2.28% of such loans, then in 2023, the maximum percentage of overdue loans rose to 4.14%.

Personal loan delinquency rates

One of the reasons for this situation is the imperfect algorithms of traditional credit scoring. 

Based solely on a borrower's credit history, they cannot provide the lender with a complete picture of a potential client's solvency.

To solve this problem, credit institutions should integrate alternative data sources into scoring models.

What is data enrichment?

Data enrichment in the context of credit risk management is the process of supplementing traditional financial information about a potential borrower with sets of alternative data from different sources. 

For example, a wide range of data allows you to obtain digital footprint analysis.

Data enrichment can improve credit scoring and make risk management more effective. It is the integrated use of traditional and alternative data that lenders should rely on.

Improve your credit scoring accuracy

With Data Enrichment

This is evidenced by the results of research conducted by FICO, one of the leading developers of analytics software. 

The diagram below, from RiskSeal's project, demonstrates that the effectiveness of credit scoring is maximized when both alternative and traditional data are used.

Credit scoring with both alternative and traditional data

This data enrichment approach allows you to create a powerful scoring model and significantly increase the accuracy of credit risk forecasting.

What is enriched data value in credit risk management?

Data enrichment is used in the lending industry to provide a more accurate and comprehensive assessment of a person’s creditworthiness. 

Here's how integrating alternative data can improve the efficiency of credit risk management.

1. Creation of improved scoring algorithms. Enriching traditional credit scoring models with alternative data can improve their predictive power and provide insights into a borrower's financial habits and stability.

2. Improved efficiency of risk assessment. Using traditional data sources in combination with alternative ones allows you to create a detailed risk profile of the borrower. 

Also, based on enriched data, it is possible to create advanced forecasting models that will increase the accuracy of default and other risk factor predictions.

3. Fraud detection and prevention. Modern Credit Scoring Solutions are capable of analyzing information from multiple sources to detect inconsistencies. Any of these could indicate potential fraudulent activity.

4. Improving financial inclusion. Thanks to data enrichment, you will be able to lend to categories of the population not covered by banking services. This is possible by creating alternative credit ratings based on nontraditional data.

5. Personalization of loan products. The use of enriched data allows the lender to obtain a detailed profile of clients and adapt the financial product to the characteristics of each of them.

For example, a high level of trust in the borrower allows him to count on more favorable lending conditions. 

The presence of risk factors forces the credit institution to play it safe by offering higher interest rates or a smaller loan amount.

6. Effective loan portfolio management. One of the capabilities of innovative scoring systems is updating data sets in real time. This allows you to adjust risk assessments and lending strategies dynamically. 

How does data enrichment occur in credit institutions?

Enrichment of traditional scoring models with alternative data occurs in several stages.

Stage 1. Selecting appropriate nontraditional data sources

Determine the optimal data sets based on your company's individual needs.

For example, for digital footprint analysis, it is advisable to use not only global online resources but also platforms popular with local users.

Stage 2. Testing data provider services

When choosing a company that specializes in data enrichment, you should not trust only the information from its website. The best option is to evaluate the provider’s effectiveness in practice.

As far as RiskSeal is concerned, we offer a free Proof of Concept (PoC), which allows you to verify the data we provide.

Stage 3. Updating an existing scoring model

Each credit institution does this by its internal procedures.

Stage 4. Evaluating the effectiveness of using enriched data

This can be done by comparing the accuracy, performance, and efficiency of the previous and new scoring models. 

Data enrichment is the best way to promote the competitiveness of a credit institution in the market, expand its target audience, and increase income.

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