Credit Bureaus Must Adopt AI-ML, Data Analytics for Holistic Credit Scores
Credit bureaus are driving significant changes in India’s lending space and financial services sector today. In India, four agencies – CIBIL, Equifax, Experian, and CRIF Highmark – provide their proprietary detailed credit reports and score for every individual. These bureaus, like other segments in financial services, are also undergoing massive digital transformation. These organizations are investing in sophisticated technologies and practices in order to predict customer behavior and lending patterns, including faster credit decisions and adding value to their clients.
We recently had a chance to interact with Pinkesh P Ambavat, CIO and IT Director, CRIF India, who sheds light on the market for credit bureaus in India and the role of technological innovation, such as AI/ML, data analytics and blockchain, in this space. Headquartered in Mumbai, CRIF Highmark claims to be India’s first full service credit bureau which offers comprehensive credit information for all borrower segments across India including retail consumers, MSMEs as well as commercial and microfinance borrowers.
How do you see the overall market for credit bureaus growing in India?
India’s credit industry has gone through a rapid evolution over the last decade and has experienced a transformation of the consumer mind-set from a savings-focused and debt-averse country to a more consumption-focused, leveraged economy. Unsecured lending has paved growth with smaller ticket sizes. Consumer inquiry volumes for personal loans and credit cards has increased significantly while inquiries are unchanged or have dropped slightly for loans against property and home loans. Today, digital transformation has significantly disrupted the nature of lending. Now, it merely takes a few touches and clicks on a smartphone screen to get a loan approved within few minutes. Lenders are constantly innovating with the latest technologies to enhance customer experience and convenience. Those who do not adopt digital acquisition in our sector may face a tough time going forward.
Also, the Covid-19 pandemic had a major impact on all the financial institutions and individual borrowers and lenders are expected to adapt to the new normal. For instance, to ease the financial impact on borrowers, RBI announced debt servicing relief using moratorium policy. To comply with RBI rules and regulation, we had to make changes to existing scoring models on priority, which ensured that there is no impact on individual’s credit worthiness due to the ongoing pandemic. Overall, I see tremendous growth of credit bureaus as there is a gradual pick up in the number of inquiries made by the customers.
What kind of challenges do credit bureaus face in India? How are you capitalizing on cutting-edge technologies to overcome those challenges?
The online lending segment in India is giving rise to a new kind of challenge on sourcing credit score data. Credit bureaus need access to alternative data sources in order to assess the creditworthiness of those borrowers who may not yet have a footprint in the formal credit sector. Several fintech companies are using Artificial Intelligence to create alternate lending data score for more than 40% of the Indian population who have no credit scores. Analytics will help companies to get market insights and build products as per customer needs. Blockchain can be used to update real time customer data in credit bureaus for any change in customer details. The experience of Blockchain technology can address gaps of the credit industry and simultaneously bring solid solutions that will dually serve both banks and consumers. Credit bureaus are increasingly focusing on custom data analytics to gain market insights and analytics. Cybersecurity is another challenge that all credit bureaus are facing which is why we relay special focus on latest security tools and industry practices to ensure utmost security of our customers’ data.
As a CIO of a leading credit bureau, what role do you play in the organization?
I am responsible for driving digital transformation through emerging enterprise technologies and strategic initiatives. I assist CRIF in providing various innovative product solutions related to open banking, antifraud solutions and digital banking business lines. I am leading several initiatives to deliver high-performance solutions by capitalizing various cutting-edge technologies. I am also responsible for overall operations, infrastructure, security, and applications with cross cultural engagements across geographies including India and Asia.
Can you share one such Aha moment in your career?
During the last couple of years, the volume of data has been increasing. This created a challenge for us to analyse the growing volume and variety of data. The traditional analytics methods could not keep up with this demand. To uncover new insights, we needed to discover new ways of looking at the data. Models that use machine learning provide details and deep understanding needed to improve credit decisions models for assessing the risks. They offer several advantages over those that use human judgment or traditional statistical models.
We have implemented machine learning in our customer matching algorithm which has definitely been the milestone of my career. One of the main benefits is the ability to run across large volumes of data to predict an outcome. However, this is not enough to produce valuable insights. The value of machine learning models lies in their relative lack of limitations. Commercial credit scoring algorithms use “machine learning” technology to integrate real-time data trends and human decision making. Another challenge that is posed is the ability to squeeze every ounce of information from different sources and using new and improved algorithms. We are the very first organisation to practice machine learning. With our key objective of digital strategy in mind, we are now working towards further improving the efficiency of the model.
What kind of technologies are you toying with at the moments? And going forward, what are you tech-related plans?
To identify new parameters for credit scoring and gather data for improved credit scores, alternative data is the key. CRIF is working on creating such partnerships for alternative sources of data. This new data will help improve the scoring mechanisms. Credit Bureaus are operation driven organizations. Robotic Process Automation (RPA) helps to automate and standardize repeat business processes. We have now started working on implementing RPA in CRIF in various business processes. To achieve high efficiency in business operations, we need to eliminate the risk of errors. RPA tends to introduce flexibility to business operations. RPA programs are installed on servers to combat this challenge, making processes flexible and scalable in case the demand shoots or the scope of a process expands. In our organisation, product support teams handle thousands of requests on a daily basis, which include creation of users, deactivation of users, help users on various requests, transfer of files amongst servers etc. All these requests were earlier handled by the support team which was very repetitive and manual. Utilizing Robotic Process Automation, we have automated these requests resulting in reducing the human touch. This in turn helped the team to focus on the innovative and challenging tasks instead of monotonous activities. Not to mention, not only the operational agility and capacity of handling the requests is improved but also the operational cost has reduced. We are also working on our custom data analysis reports using our analysis products, which will help in providing analytical solutions to newer clients to tackle their business problems.
Source: Publication: CXOtoday.com, 16th Feb ,2021