How Lenders Can Use Location Insights to Reduce Default Rate

Staying competitive in the modern consumer lending market can be challenging. But what if there was a solution that could enhance your credit scoring and give you a significant advantage over other lenders?

Location analysis is such a tool.

Understanding that not all locations are created equal is crucial. Economic conditions vary widely between urban and rural areas, and even within cities, there are neighborhoods that differ in prestige and economic stability. 

This variation directly impacts credit risk, with stronger economies typically correlating with lower default rates.

By integrating location insights into scoring models, lenders can make more informed decisions, reduce default rates, and ultimately gain a competitive advantage. 

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The importance of location insights for credit risk management

The default rate on loans is influenced by the economic conditions in a specific region.

USA

As an example, let's look at the credit card delinquency rate in various US states. According to VisualCapitalis, it ranges from 12.9 percent (in Iowa) to 39.1 percent (in Mississippi):

Mexico

In developing countries, this ratio is usually higher. Let's look at Mexico's loan portfolio as an example.

According to a FINCA Mexico report, the level of arrears differs depending on the city where borrowers live. For example, in cities in the southeast of the country, more than 50 percent of transactions are in arrears.

While in other cities, the number of problem loans is much lower. For example, in Puerto Escondido, Oaxaca, and Cuatro Caminos the delinquency rate does not exceed 12-13%.

India

Similar dependence of borrowers' solvency on economic development in the region of residence can be traced in India.

A report by the Fintech Association for Consumer Empowerment (FACE) states that the default rate on consumer loans in India's megacities is 3.3 percent. While in rural areas it is noticeably higher at 4.1 percent.

The number of delinquent borrowers also varies depending on the state. For example, in Tamil Nadu, this figure is the lowest at 3.1%, while in West Bengal and Rajasthan, more than 4% of borrowers fail to repay loans on time and in full.

This statistics proves how crucial it is for lenders to enrich their scoring models with location information.

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What are location insights in credit risk?

Location insights involve verifying the borrower's geographic location and comparing the obtained data with what is stated in the loan application.

This technique allows the assessment of an applicant's reliability and creditworthiness. The obtained data enable the lender to:

• Compare the residence address provided in the loan application with the IP location information. A mismatch may indicate intentionally incorrect information, suggesting potential fraud.

• Assess the likelihood of default based on the applicant's region. Depending on the unemployment rate, the average number of delinquent loans, and other economic indicators, it is possible to predict whether the borrower will repay the loan on time and in full.

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How location insights work in credit scoring

This technique is successfully used by alternative data providers. The borrower verification process using this technology involves several stages.

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#1. Provision of potential borrower data

The collaboration between the lending organization and the alternative data provider begins with the transfer of the information provided in the loan application.

E.g. RiskSeal uses the following data for verification: 

• applicant's first and last name
• borrower's IP address
• home address (optionally) 

After location insights analysis, lenders receive an extensive borrower profile that includes:

• current device location
• IP address geolocation
• selfie location
• location identified via digital footprint analysis

A positive signal would be the submission of a loan application directly from the applicant's residence, meaning the current device location matches the home address provided.

This fact confirms that the borrower is indeed the person they claim to be, assuring the lender that it is not a fraudster.

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#2. Applicant location analysis

At this stage, the alternative data provider performs geocoding, obtaining the precise coordinates of the applicant.

This allows identifying the exact point on the map where the potential borrower is located at the time of the loan application.

Lending organizations also have access to the reverse service—reverse geocoding. This involves determining the applicant's address based on the available coordinates.

Using reverse geocoding, the alternative data provider returns the following information about the borrower's location:

• country
• city
• street name
• house number
• location type

With location insights, it is possible to determine precisely where the applicant is currently located, whether in an office building, apartment building, park, prison, hospital, bar, etc.

The obtained data are also used to identify discrepancies in the provided locations.

If all addresses are in the same region (country, city, state, etc.), it is considered a positive sign, indicating the borrower's reliability.

Significant differences can be a reason for concern and may be indicative of fraud.

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#3. Data accuracy assessment

Based on the conducted verification, the lender compares the data obtained from various sources to conclude the borrower's creditworthiness and reliability.

The lending organization should measure the distance between the provided addresses and check the applicant's stated and determined locations. All the obtained results are important to consider in the scoring model for an objective consumer assessment.

The lender also needs to monitor where applications are coming from. For example, many applications from one location raises suspicion, a sort of red flag.

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#4. Analysis of the potential borrower's region of residence

Checking borrowers' locations is not limited to comparing locations. Specialized platforms also analyze the region from which the loan application was submitted.

This is an essential feature because each region differs in economic and social indicators that directly affect the creditworthiness of potential borrowers.

1. Standard of living

When calculating the Quality of Life (QoL) Index, various data are considered, including income, cost of living, and purchasing power of citizens.

This indicator varies significantly by region. 

In the Netherlands, Luxembourg, and Iceland, it is highest at 190–200 points, while in developing countries, it is substantially lower. 

For example, in Mexico it’s 125, in India - 116, and in Nigeria - 49.5 points.

The schematic map below clearly demonstrates how the QoL Index differs across various countries worldwide:

In their scoring models, lenders should consider that residents of economically developed regions will demonstrate better creditworthiness due to higher income levels.

2. Unemployment rate

This is another critical indicator for credit risk management. The more employment opportunities available to a potential borrower, the more likely they are to make timely loan payments.

Unemployment rates also vary across different jurisdictions.

The highest employment difficulties are observed among residents of South African countries. According to Trading Economics, 32.9% of the population in these regions is unemployed.

In India, Nigeria, and Mexico, the situation is less dire. In these countries, 7.6%, 5%, and 2.6% of the population, respectively, are not employed.

3. Crime rate

This indicator is not as immediately obvious in its value for credit risk management as the previous two, but is still necessary to consider.

Higher crime rates in a region increase the likelihood of encountering fraud when issuing loans.

To determine crime levels, similar to the Quality of Life Index, there is a Crime Index, which represents the number of crimes per 100,000 residents. 

This index varies for each country around the world.

According to statistics, the highest crime rates are observed in Venezuela, South African countries, Afghanistan, and others. In these regions, the Crime Index exceeds 75 points. 

In India, this index is 44, in Mexico it is 54, and in Nigeria it is 65 points.

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Location insights with RiskSeal

RiskSeal's scoring system specializes in enriching credit institutions' scoring models with location information. 

We perform geocoding and reverse geocoding to determine the coordinates and exact address of potential borrowers.

This technology, combined with other tools such as IP lookup solutions, allows RiskSeal clients to:

• Identify the risk of fraud at the loan issuance decision stage.
• Obtain an objective assessment of applicants' creditworthiness and solvency, thereby expanding their target audience.
• Personalize credit products based on the risk level assigned to the borrower.