Consumer Finance Case Study

Big Data and smart analytics can reduce your collection costs and increase margins - especially in Consumer finance.

How you can you leverage Big Data and analytics to increase your financial performance? Focus on simple, targeted analytics to begin with and incorporate it into your automated decision making systems. Find out how we changed the financial performance of two financial institutions in South and Southeast Asia, first by cutting collection costs and the second by decreasing risky loans by making onboarding seamless.

Rosebay has worked with several financial institutions in the region including Post Office (Finance) Indonesia, one of Indonesia’s leading financial services and logistics company, which has in its mandate not only delivery of parcels and letters but also financial services delivery including branchless banking, payments, insurance and monthly installments.

The Challenge

Our customer, a multi-finance company in South Asia services over 2 million customers with a focus on unsecured loans for electronic and mobile appliances as well as automotive loans. While the number of customers had been increasing year by year, the management was concerned regarding a steady deterioration in key financial metrics.

Delinquencies 2.3% over industry leader for similar financial products
NPL 1.7% above industry leader for similar financial products

This was surprising to the management as new risk management policies ushered in just 18 months back were expected to improve underlying metrics.

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Consulting & Analytics

Rosebay was chosen as a consultant to use data and evidence based approach to

Analyze the manifested issues
Identify the underlying business problem
Design effective solutions to increase financial performance

Of particular appeal to the client was Rosebay’s agile data centric methodology as causes of business problems are seldom single faceted. Solving real world problems necessitates a discovery driven approach based on actual data evidence before committing large investments.

Over the course of four months, Rosebay worked with a cross disciplinary team, analyzed several data sets of the customer as well as the industry and held focus group sessions to identified the underlying issue. Contrary to customer’s expectations our analysis and evidence pointed strongly to lack of rapid financing decision support systems as the underlying cause for performance deterioration.

The Solution

The solution called for a radical departure in the way loans were judged and disbursed. Given the findings our consultant worked with the management to define service levels for loan servicing which were divided into three tiers based on competitor and industry analysis:

Tier 1. Under 2 min processing for 85% of accepted applicants
Tier 2. Under 25 min processing for next 10% of accepted applicants
Tier 3. Under 24 hours processing for final 5% accepted applicants

Tier 1 processing in particular could not be accomplished with current technological capabilities of the customer. A cost benefit analysis was performed and it was determined along with management that investment in analytics was warranted given the trend in the industry (competitive pressures) as well as expected financial benefits. The solution consisted of:

1. Establishment of Data hub architecture with Big Data capabilities
2. Emphasis on technology that permitted near real-time processing
3. Pre-processing and caching of intermediate results at off peak hours to ensure super fast processing which minimizes investment
4. Way to calibrate risk so that system could be fine tuned without expert data science assistance (parameter driven fine tuning)
5. Rapid reporting systems for management and risk teams.

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Effect

Once the systems were implemented and results within service levels over a period of twelve months we noticed

Delinquencies were down 3.7% controlled for other variables
NPL was expected to decrease by 1.5% based on other KPIs
Collection costs decreased over 10%

The RoI was over 100% within first twelve months of the rollout

Our Services

Reduce NPL

Covid19 has had a significant impact on banks’ and financial institutions NPL, especially in the personal loan and unsecured loan markets. Data and evidence based approach can improve financial performance via better risk management and automated selection of applicants with the correct risk profile to meet your targets.

Control Collection Cost

Collection costs tend to be particularly problematic in consumer finance segment for small ticket loans such as mobile financing and uncollateralized small personal loans. Big Data and AI can help predict and design better loan portfolios that can help control collection costs.

Personalize financial products

A one size fits all approach does not correctly judge loan risks. Modern data analytics make it possible to personalize loan products to make it a fit for applicants’ risk profile. Real-time analytics systems assist in designing fit for purpose financial products while increasing margins.