Definitions

Credit Analysis is an evaluation of the creditworthiness of an individual or business or organization.

Credit Risk Scorecards are mathematical models that attempt to quantify the creditworthiness of a customer.

A Credit Score is a number between 300-850 that depicts a customer’s creditworthiness. The higher the Credit Score, the better is the creditworthiness of the customer. In simple terms, a customer with a high credit score can easily get credit from Banks/NBFCs.

In Banks/NBFCs, a Credit Officer means a person who executes the credit process. In some financial institutions, a Credit Officer is also responsible for acquiring new customers and helping them navigate the institution’s loan application process. In this blog, we will consider the role of a Credit Officer as someone who does the credit assessment and analysis.

Applications of Credit Risk Scorecards

• To assess the credit risk of a customer applying for a loan, credit card, or line of credit
• Ongoing assessment of an existing borrower (customer already having a loan, credit card, or line of credit)

The concept of Credit Scorecards is extensively used by Banks, NBFCs, and Fintechs. The Ecommerce companies use it to decide the customers to whom they should provide a Cash-on-Delivery option. The concept can be easily applied to Manufacturing, Trading, Services, Ecommerce, Retail, and other businesses where products and services are given to customers on Credit/Cash-on-Delivery.

Credit Risk Scorecard in Lending Business

In the lending business, the sanctioning of a loan or a credit card is done based on the credit evaluation of the applicant. Traditionally, Credit Professionals (Credit Officers) having good analytical skills in credit analysis are hired for credit evaluation of loan applicants. This practice is not yet obsolete and many lenders continue to run the business in traditional style. However, there are certain limitations of manual credit evaluation:

1. The process is time-consuming and monotonous
2. It is expensive, especially for small-ticket loans or line of credit
3. It relies heavily on the judgment and analytical skills of the Credit Officer, leading to subjectivity in the decision-making process
4. The process is not flexible to enable risk-based pricing
5. The feedback loop is restricted to individual Credit Officer’s memory
6. Learning restricted to an individual’s experience

Credit Assessment 2005 vs 2020

Based on inputs from the banking industry veteran, Mr. Vinod Kondurkar, we provide a comparison of the Credit Assessment process between the years 2005 to 2020.

Activity	2005	2020
File Sourcing	Paper-based file	Digital files
Income Assessment	Judgmental	Template
Banking Assessment	Manual	Digital – Outsourced to companies providing Automated PDF & OCR parsing services,
Reference check	Personal Networking and Field Visits	Field Visits Social Media (Facebook, LinkedIn) Social Security (Aadhar, Pancard, etc.) Digital Customer Profile (Amazon, etc.)
Query resolution	Telephonic Face to face	Digital (Video call, Scanned documents, Photos, Location Mapping)
Credit Discussion	Travel to customer location	Digital
Credit Bureaus	No	4 options for credit score (CIBIL, CRIF High Mark, Equifax, Experian)
Scorecard	No	Multiple Credit Risk Scorecards
Rejection	Manual	Machine
Intelligence	Human Intelligence	Artificial Intelligence
Decision Process	Individual Memory	Enterprise-wide memory
TAT	20 – 30 files per Credit Officer per day	125 -160 files per Credit Officer per day

Given below is a more detailed articulation of the above points.

1) Time-consuming and Monotonous

I am sure this point is very self-explanatory. A borrower applying for loans submits various documents to prove his/her creditworthiness. The document list includes know-your-customer (KYC) proofs (like AADHAR, PANCARD, etc) bank statements, income tax returns, asset details, etc depending on the type of loans. The credit officer has to go through various documents, spreadsheets, and perform customer due diligence before approving/rejecting the loan applicant. This manual process of credit evaluation is not only time-consuming but also monotonous because the task is of a repetitive nature. It may sometimes lead to erroneous decisions due to fatigue.

2) Credit Evaluation process is Expensive

The credit due diligence takes on average around 30 minutes to complete for a Personal Loan file provided all the loan documents are intact. At this rate, a Credit Officer will be able to assess only 16 – 20 files a day.
Secondly, the loan disbursal to application ratio is about 40%. The remaining 60% of loans either get rejected by the financial institution or the customer declines to take the loan. These factors make the credit evaluation process expensive. The problem gets compounded if the loan ticket size is small. As such, financial institutions need quick turnaround times and low-cost economical solutions for credit evaluation.

3) Objectivity vs Subjectivity

The traditional process relies heavily on the skill and acumen of the credit officer. It is quite possible that the same loan file may be accepted by one credit officer and rejected by others. It is also possible that the same loan file may be accepted/rejected by a credit officer depending on his mood and emotions at the time of decision making. All these factors add subjectivity to the decision-making process.

The benefit of a Credit Scorecard is that it quantifies the risk by way of a Credit Score. Moreover, the scorecard will give the same credit score for a given set of input parameters, it is devoid of human moods and emotions. As such, scorecards bring the benefits of objectivity and transparency in the decision-making process.

4) Risk-based Pricing

The traditional credit evaluation process being subjective does not lend itself to risk-based pricing. Credit Scoring quantifies the risk, thereby you can correlate the loan default rate with credit score and build risk-based pricing strategies.

5) Feedback Loop

The financial institutions provide continuous feedback and insights to the Credit Officer on the performance of the loan portfolio sanctioned/approved by them. However, the ability to assimilate the feedback is restricted to each individual’s memory and abilities. For e.g., if the borrower defaults within a month or two after taking the loan, the case and its details may be fresh in Credit Officer’s memory. S/he will be able to better realize the mistake made and course-correct for the future. However, if the case is 6 months old or more then it may become difficult to remember each and every case and learn from them.

6) Individual Learning vs Enterprise Learning

The financial institutions provide periodic training, review meetings, and various forums to ensure their Credit Officer learns from each other’s experiences. However, the discussions in these educational and review sessions often are restricted to a few big-ticket default cases. As such, the learning of Credit Officer narrows down primarily to their individual experiences.

Credit Scorecards are built by taking organization-wide data. As such, enterprise-level learning gets captured in the scorecard.

Will Credit Officer Role become Obsolete?

No, we are not saying that the Credit Officer role will become obsolete. It will continue to exists. In fact, Artificial Intelligence is enabling Human Intelligence in taking faster and better decisions. Lots of Banks/NBFCs are using Artificial Intelligence in credit decisions for small-ticket credits like Home Loans, Personal Loans, Car Loans, Credit Cards, etc. Human Intelligence will still be required for sanctioning big-ticket loans, corporate financing, project financing, etc.

How to build a Credit Risk Scorecard?

We will discuss credit risk scorecard model development and automating the credit decision process in our upcoming blog. Till then stay tuned.

Thank you

We hope you enjoyed reading this article. Recommend reading for you:
1. How to design a Customer Single View?
2. Automated PDF Bank Statement Analysis

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About Vinod Kondurkar

We would like to express our appreciation to Mr. Vinod Kondurkar for sharing his knowledge and experience with us in writing this blog.
Vinod Kondurkar has 30+ years of experience in Banking and Financial Services. He has worked with some of the largest MNC, NBFCs & Private Sector Banks. His experience spans across all retail banking loan products and functions – marketing, credit & collections. He has set up the Direct Marketing Cross-Sell channel for a number of financial institutions using Data Analytics. Vinod holds a Diploma from NMIMS in Finance Management.