DATA GOVERNANCE –A PATH TO BETTER CREDIT ANALYTICS

CONTENTS

CUSTOMER CREDIT -DATA PROBLEM

DATA ANALYSIS AND RECOMMENDATIONS

IMPACT ANALYSIS CONCLUSION

CUSTOMER CREDIT - DATA PROBLEM• One of the striking problems in medium and large Multi-finance companies in Indonesia is sourcing and utilizing the Credit history of customers in an efficient and cost-effective manner.

• PEFINDO is a locally owned domestic credit rating agency in Indonesia that rates individuals, corporates and government agencies. They have a strategic partnership with S&P since 1996 and adopt credit rating methodology used globally.

• Customer Credit data like last reported income, exposure, outstanding time buckets with banks and Financial institutions are provided by PEFINDO through API which is used by banks and finance companies for Risk Scoring.

• In multi-finance risk scoring, these attributes are used for evaluation of A-score (application scoring), B-Score (behavior scoring) and C-Score (Collectionsscoring). These scores are used for Application processing, new product offering (cross selling) based on behavior and fine tuning the collection/new application process, respectively.

• Ingestion of large data sets with more than 180 attributes across the universe of customer records makes it humungous task to formulate a right model for storage, retrieval and analytics.

• If managed efficiently, this information improve new business generation and better recovery process.

Areas of improvement for financial institutions

Risk and collections can have a more accurate prediction of potential write-off or recovery for NPL (Non-Performing Loans). With limited internal loan exposure data – the customer behavior with other banks/FI are overlooked and creates decision bias.For e.g.: A customer who has more than 210 days past due payment with one bank/FI can still be in good payment bucket with another bank/FI, which indicates that he/she is not a potential write-off customer but collections process has to be made bit more efficient.

Generation of Repeat ordersfor high-value customerswith larger exposure basedon analysis of behavior scoreand repayment history withother banks/FI. A significantincrease of up to 35% can bebrought up in success ratesof potential new orders fromexisting clients with a propertime bucket analysis.

Lean Business Processenablement with near-real time scores i.e., less scrutiny for clients with better A, B and C scores provides faster turnaround time for new applications.

HIGH VALUE CUSTOMER- REPEAT ORDER

PEFINDO SCORING FOR LBP

2017-2018 (Before), 30%

2017-2018(After), 60%

0%

10%

20%

30%

40%

50%

60%

70%

2017-2018(Before)

2017-2018(After)

POTENTIAL RECOVERY %

WRITEOFF – RECOVERY ANALYSIS

Before, 36%

After, 70%

0%

10%

20%

30%

40%

50%

60%

70%

80%

Before After

Customer VVIP

• Near Real-time data feeds from credit agency for all attributes• Calibration of Risk scoring model with Historical data• Detailed usage of time-bucketed exposure with other financial institutions and banks

Case Study from a Multi-Finance company in Indonesia

Green1 Green2 Green3 Green4 Green5 Regular Reject

% Scoring 13% 18% 7% 16% 21% 17% 8%

0%

5%

10%

15%

20%

25%

% Scoring

Where is the customer credit analytics heading to ?

With an internet penetration of more than 65% (in Indonesia), the rate at which consumers move from traditional venues to fintech and mobile e-commerce venues for their lifestyle requirements is rapid.

Whereas the financial institution has data from traditional venues like Banks and FIs through credit rating agency, actual spend pattern on venues like Go-Jek, Grab, Traveloka and Tokopedia – can make it efficient to predict and prescribe when the customer is moving towards a default stage or is more reliable to be able to offer more products in future.

Conclusion – Without proper data governance practice and data analytics tools, financing companies can become blind very soon which might result in loss of business or even closure.

Embracing the fintech solutions along with traditional ones will lead to smart business development and growth in revenues.