Digital Lending, Are you on board?

The business of money has come a long way. As an example, the home loan industry has grown from the 3-6-3 business model (borrow money at 3%, lend money at 6% and arrive at the golf course at 3 PM) to the 3-1-0¹ model (apply for loan in 3 minutes, get the money in 1 minute with 0 human contact). 3-6-3 model ruled the roost when Germany was divided by a wall, China was not vying to be the biggest economy, Internet was not in anybody’s vocabulary and Artificial Intelligence (AI) was still a dystopian future presented by Hollywood directors. But, the market has become digital (View toCode’s Quick Customer Onboarding Solution) and the increasing digital savviness of customers, millennial and old people alike, has led to the commoditization of products from food to loans.

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A customer no longer applies for a loan. She purchases a loan!

In a report released by BCG², digital lending as an industry is supposed to grow to $220 billion by 2020. Firms have started looking at their core competency differently. Jack Ma has started looking at Alibaba as a data company. The central theme surrounding the reimagined business models is digitization. In the BCG Survey conducted among users who had purchased loans in the 12 months preceding it, 1 in every 2 customers irrespective of age, gender, and occupation had been influenced by the digital market. With the FinTech space, especially in retail loans, mushrooming exponentially, banks have started giving their attention to data. In fact, legacy banks are probably best placed to take advantage of this situation. The data in such banks is either internal or external. External data is available to the general public. But, it is the internal data that gives a bank its competitive advantage. Of course, the monetization of this advantage depends on how a bank uses the data. If a bank can get a customer’s consent, and use the data in the right way, it can see a huge transformation in its business.

The key to the future is the ability to take right decisions instantly.

This is akin to choosing between a call to fight and a will to flee when attacked by a tiger. A correct decision in this instance is dependent on how the body processes the genetic codes and our knowledge of the world to take an intuitive call; In this case, intuition is the ability to synthesize disjoint information to a coherent binary choice. Similarly, we believe in using the huge amount of data that is at the mercy of a financial institution and use Artificial Intelligence and Big Data to help a bank change the way it offers customer experience, performs its business operations and takes business decisions.

The transformation towards digital lending should focus on 4 major areas:

1. Customer Experience: AI is the new UI

One in two customers purchased loans online2 and the scope of digital lending is expected to expand further. Two-thirds of the world’s population now has access to mobile services2. There are different business models from different FinTech companies that offer multiple avenues to get loans cheaper and quicker. These FinTechs have built business models that can disburse loans in 10 minutes from the time of registration! In short, retail borrowers can access credit more easily.

The single thread that makes all of this possible is the experience offered to customers.

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A majority of the retail customers no longer expect to be hand held by a relationship manager. At the same time, they expect products that are simple to understand and tailored to their specific needs, and which can be easily understood by researching online. Hence, it is important to minimize human touch points when customers would rather increase their digital footprint than to visit branches physically, digitize the KYC process such that the submission and verification of documents involve minimum to zero human interaction, or better still, is reduced to the bare minimum that is expected from regulators without compromising on the robustness of the verification process. To cater to these expectations, we need to mould technology that shall address this changing customer behavior and offer a more enriching experience that improves the onboarding process.

2. Process Optimization: Time is Money

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No financial transformation is complete without business process re-engineering.

38% of the applicants had chosen a service provider because the loan processing was faster while 1 in every 3 customers had selected a service provider because of lesser documentation

Obviously, these two factors play a part if the service is digitized. But, many customers still prefer to visit the banks which means zero human touch, in the current context, can still leave a third of the customers unserved. To rephrase, while optimizing a process, it is important to be cognizant of the limitations and associated opportunity costs of end-to-end digital lending, and build business models that do not compete, but complement the existing models. At this point in time, a business model should enable a bank officer and cannot aspire to replace a bank officer. Of course, it should focus on making a process leaner. Banks must ask the right questions to optimize the process.

1. In a highly competitive market, how can we leverage our reputation and customer base to expand further?
2. How can we reduce the time to market?
3. Are credit scores omnipotent?
4. How can we expedite the process of loan disbursement?
5. Is there a possibility of pre-approved loans for a customer, if not available already?
6. How can I use the humongous amount of data, including point-of-sale transactions to build a risk model that not only analyses the risk, but also, recommends customized products?
7. How does effective communication with the loan applicant happen in a digital ecosystem?

3. Persona Analysis: Highly personalized segment of one

There is a uniqueness to the individual customers. But, there is a commonality between the individual customers as well. That is, what a customer seeks might be determined by group behavior while how he achieves it might still be an individual choice.

Highly personalized ‘segment of one’, although a distant dream, is an industry aspiration.

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Is it possible to leverage behavioral economics to design bespoke products? How do we glean individual personas from clusters? A highly correlated cluster can offer insights into individual patterns and future behavior. Where the data is unavailable, especially for new customers, or where individual risk profiles cannot be built based on existing models, analytics can help in identifying commonalities between different, but unique customers. An individual persona around a customer’s credit worthiness can be built by using publicly available data, data from the aggregators and internal data within the bank (for an existing customer). Growth patterns such as average increment, spend patterns, expense distribution, personality traits, qualifications, nature of point-of-sale transactions etc… of an applicant can be used to build bespoke products that will not only cater to the current needs, but also, spawn new products to address gaps that may have hitherto remained undiscovered.

4. Risk Analysis: For the Unserved and Underserved

Risk profiles of customers can be business, sector, country and region specific. Existing risk models may determine the number of expected defaults in a portfolio of loans and determine the total exposure and the total loss given default. But, a Big Data ecosystem and the impact of regulations like Open Banking Act will help in creating new financial indicators that will supplement the traditional financial indicators. Risk profiles can become individualized and more granular. An advanced risk score can be created to complement credit scores like FICO scores. The individual personas can create individual risk profiles by using these advanced risk scores. In fact, the predictive capabilities of these models can help in identifying a willful defaulter in advance along with an applicant’s propensity to default. The risk profiles can help in creating new products that can cater to the short term and long term needs of the individual.

The idea is to find a market of deserving customers that are unserved and under-served.

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Of course, the risk profiles and the risk scores do not work in isolation. At the end of the day, the applicant is part of a credit portfolio which will be leveraged to grant a suitable loan. In other words, we can determine if a product that the applicant wants to buy can potentially become a toxic asset. In such scenarios, instead of branding the applicant as a credit unworthy customer the risk score, individual persona and risk profile can be used to identify another product that may cater to the applicant’s needs without compromising on the risk.

To conclude, retail banking has been blessed with a lot of innovations in the last decade. Changing consumer behavior, technological improvements, proliferation of data and new regulatory and compliance laws have created a favorable environment for digital lending. Organizations that are prepared can take advantage of this market that is expected to grow quickly.

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