How to Leverage the Power of Alternative Credit Data in Banking

Make Insightful and Reliable Credit Decisions with FinScore

Leverage the Power of Alternative Credit Data as Advanced Analytics

In today’s competitive world, growing your customer base and client retention rate are challenging tasks. Banking products are getting commoditized and features such as savings accounts, personal loans, and credit cards are becoming more similar by the day.

How then can banks stand out and grow? The answer is simple: through the use of alternative credit scoring data in combination with predictive analytics to determine the creditworthiness of customers.

 

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Extending Credit to the Unbanked and Underserved With Alternative Credit Scoring

Due to lack of customer data or history to assess creditworthiness, many commercial banks often hold back from lending money to underbanked individuals and small businesses. However, more and more of the underbanked and underserved have the ability to pay their bills and repay loans on time. Traditional credit data sources can miss to cover them. For these individuals, it’s time for banks and other financial institutions to explore new data analytics practices to reach the credit-invisible market.

FinScore has the power to do that for you through our alternative credit scoring models. We use alternative data to provide additional insights for decision making. These include telco data such as data and voice usage, location, top-up patterns, and more. FinScore’s alternative credit scoring can be used to incorporate data analytics into a bank’s algorithms, making its credit scoring systems more robust. This helps banks and other financial institutions serve even those with non-existing credit history. The Credit Score can be used for different products: credit cards, cash loans, purpose bound loans, motorcycle loans, and more.                                         

FinScore_Telco Data Credit Score

Furthermore, the Telco Credit Score model can also be used as predictive analytics in banking to help identify fraudulent activities, aid in application screening, capture relationships between explanatory and predicted variables from past behaviors, and use all of these data to predict creditworthiness. As machine learning technologies improve by processing more and more data, these predictive models will become increasingly reliable methods of assessing creditworthiness and are likely to be increasingly adopted by both incumbent and alternative lenders.

Telco Data: The Gateway of Rural Banks to Better Service for All

Rural banks in the Philippines are catalysts for financial inclusion in the country, bringing more opportunities to far-flung places. It is crucial for the rural banking industry to leverage innovative technologies such as alternative credit scoring to enhance existing service delivery channels and further expand market reach.

Telco data is very useful in predicting an individual’s risk level without a formal financial history or credit history. The large set of data that can be collected from telcos and used as banking analytics for determining creditworthiness include:

Prepaid or Postpaid Service:

SIM activation information, postpaid defaults, mobile wallet information, and tenure of customer.

Data Usage:

Data used, hourly usage of data, applications used, websites browsed, revenue generated from data, and data related value-added services.

Geolocation:

City area and barangay-level location

Top-Up History:

Top-up information, top-up channel alongside size and frequency of top-ups, bill payment channel, invoiced amount, mode of payment, and payment terms.

Calling/SMS Patterns:

Statistics on call count/duration, number of SMS sent and received, inactivity, and calling consistency.

Demographics:

Demographics information recorded at the time when the customer filed for the SIM card, including inferred data.

Using telco data as a type and advanced analytics helps the banking sector assess if credit-invisible consumers can qualify for loans, especially those in the rural areas with no existing credit report to back them up.

 

Telco Data Scoring and Fraud Detection Solutions

Using Both Traditional and Alternative Data to Increase Accuracy in Credit Scoring

While traditional data helps lenders get a sense of an individual’s financial reliability, alternative credit data offers a broader view of a consumer’s credit behavior beyond reports given by credit bureaus. This current and comprehensive visibility into consumer risk allows financial institutions to deliver smart, accessible, and optimized offers for credit and other services. Combining the use of both alternative and traditional credit data sources will result in a more accurate model. This helps financial firms separate risky clients from lendable ones — even for the full-file population — making it much easier for financial institutions to reduce lending risk.

Additionally, as the use of alternative credit data as predictive analytics in banking will help you reach more prospects by leveraging alternative data to determine their creditworthiness. In some cases, you’ll only be able to use alternate credit scoring data to assess their applications. With information on customer behaviors, preferences, and movements, banks can also increase profits and revenues – from product development and sales to marketing and customer service.

 

Finscore’s Alternative Telco Data Improves Data Analytics
in the Banking and Finance Industry

To gain a competitive advantage, banks need to recognize the potential of alternative credit data as data analytics, incorporate it in their decision-making process, and develop strategies based on the actionable insights from customer data.

At FinScore, we use cutting edge AI and machine learning technology for our telco credit scoring system. It’s built on over 400 various telco data variables such as data and voice usage, top-up patterns, location, SIM age, and more. It can be used as a standalone model or combined with existing score models.

Since 2018, FinScore has been the leading credit scoring company in the Philippines. We have a long-standing partnership with SMART, the trusted telco and digital services in the country. As of December 2020, we have delivered over three and a half million credit scores to partner institutions by leveraging telco data and advanced analytics. Our digital credit scoring models and fraud detection solutions help financial firms increase approval rates, reduce faults, and combat fraud.

Credit Scoring System

For more information and enquiries about our telco data scoring system, contact us today.

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FinScore is a financial technology company in the Philippines that offers a powerful credit scoring platform and fraud detection tool based on alternative data, including telco-based data. 

As the pioneer in lending and scoring of the unbanked, we continuously provide fintech services that empower financial institutions, banks, and credit bureaus with flexible platforms to help them make insightful and reliable credit decisions. Contact us today to learn more about our products and solutions for financial institutions.

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