Digital innovations and disruptions- a panacea for today’s evolving fintech landscape

In today’s evolving fintech landscape, emerging technologies are not just nice to have but imperative for companies to remain relevant. To ensure frictionless and secure transactions of large volumes, one can’t rely solely on human intervention. To effectively mitigate risk, one has to build and work with newer technologies on a real-time basis to serve customers better.

“This is especially true for a company such as PayPal where we work with 267 million consumers and merchants across 200 geographies. Over the course of 2 decades of experience, we have built advanced machine learning and deep learning algorithms with dual networks as well as deep learning with thousands of features that will enable the system to take informed decisions on whether a transaction is good or bad,” says Kishore Konakanchi, Head of Product & Engineering, PayPal India, a US-based worldwide online payments system.

Emerging technologies are changing the way we live, shop, travel and make transactions. Technologies such as machine learning and artificial intelligence are making our lives effortless and secure.

Emerging technologies like blockchain and smart contracts have helped companies like IndiaLends to enhance the security of consumer data and make the digital lending process more transparent. “For the identification of the borrowers, blockchain has been serving as a huge solace. Users can enter the data on the digital platform which is verified, protected with the help of passwords and encryption and then passed on to the blockchain. Users can also choose which information to be shared with which lender,” says Gaurav Chopra, CEO and Founder, IndiaLends- a digital lending and borrowing marketplace.

To ensure that the entire lending process is transparent and clear to its customers, smart contracts come into play. And this is, in fact, more feasible for it. Instead of using cumbersome electronic data storage systems, the company can record all the information on blockchain.

Blockchain is also used extensively in an Indian online life insurance and general insurance comparison portal. “At the moment, the Indian insurance industry loses $6.25 billion or nearly 10% of its revenue attributed to various frauds, of which 86% are attributed to the Life insurance sector, which is more than six times of general insurance segment that witnesses frauds to the tune of 14%. By integrating blockchain technology, is looking to detect and minimize the rampant frauds that are on-going in the insurance ecosystem affecting both the business and the consumer,” says Rahul Agarwal, Chief Technology Officer,

At the moment, consumer redressal takes time due to varying IT systems used by different stakeholders in the system, which leads to non-uniformity of data.

Financial technology company OBOPAY understood the value of Blockchain much before Bitcoin made it a popular technology. OBOPAY has been securing all the financial transactional records using Blockchain for almost over 4 years now. Blockchain enhances the company’s product by securing transaction using validation keys that are linked to previous transactions hence protecting them from any unauthorised changes.

A single change to any transaction invalidates the whole blockchain. This ensures authenticity of transactions on Obopay financial platform.

Cognitive Science too is gaining traction among fintech companies. In a world where the customer is king, the analysis of customer behaviour is the key to acquiring the approval of the king. “Cognitive science and user behaviour analysis is the way for the future. Analysis of customer’s action on our digital platforms not only help us in enhancing their experience but also makes it easier to showcase the products that are best suitable for their needs and lifestyle,” says Chopra of IndiaLends.

Square Capital- India’s largest integrated mortgage marketplace has developed online capabilities to provide accurate eligibility of an applicant linked to the credit bureau and various banks’ products and credit policies, ability to perform e-KYC of loan applicants, integration with NSDL and OCR-based document reading and direct integration with banks’ loan origination systems. “At Square Capital, we are testing Cognitive Sciences derived modules to calculate the probability of successful loan disbursement to a client, based on his/her demographic information such as location, loan amount requisitioned and acquisition channel. This information clubbed with the recent interaction data of the client with our on-ground sales team is taken into account to improve the model,” says Tanuj Shori, Co-founder and CEO of Square Capital.

Robotic Process Automation (RPA) too is being widely used by fintechs. Previously the digital lending industry had no option than to rely on complex legacy systems, spreadsheets, multiple databases, and manual processes. “RPA is paving a new way of making digital lending space better. It not only offers a significant opportunity to further drive value but also add efficiency. From the testing of both product and design to formulating statistical test framework, RPA is being used in several spheres on the app-building process. RPA provides an efficient and fast way of analysing the effectiveness of any process that we are working on and also the efficiency of the product flow we design,” says Chopra of IndiaLends.

SBI Card has also implemented RPA that allows them to optimise the collaboration between human employees and robots, contributing towards an authentic digital transformation that also improves efficiency, accuracy and scalability. “We started our journey with RPA with Customer Services function viz ‘Transaction Dispute Follow-up process’ and after witnessing significant benefits such as reduction in human errors, increased process efficiency and enhanced customer experience through early action and process re-engineering, we extended the platform to other processes as well,” says Hardayal Prasad, MD & CEO, SBI Card.

The basic concept of machine learning (ML) is very crucial to the digital lending sector. Collection of large amounts of data and the consolidation and assessment of this data is essentially crucial in this space. “From portfolio management of our consumers to detecting frauds and possible threats to our lenders, machine learning is proving to be a boon to the sector. The algorithms that we are using to provide the best offers to our users and to identify the risk associated with the applicants are continuously evolving and machine learning is the driving force behind that evolution,” says Chopra of IndiaLends.

Square Capital, together with Square Yards is also developing a proprietary ML-based model for ‘Intelligent’ lead assignment. This model takes into account a variety of factors such as a sales representative’s skills, win ratios and experience level/maturity for similar customers. Factors like territorial knowhow, responsiveness (FTTR, call time, F2F feedback), stage in the sales pipeline, revenue forecast, marketing campaign or acquisition source to improve the experience provided to customers. “Needless to say that such new tech frees up our internal resources to get our teams to focus on value additions only, improving revenues and overall customer experience, which is paramount to us at Square Capital,” says Tanuj Shori, Co-founder and CEO, Square Yards.

ML is helping Policybazaar in a number of ways like enhancing the customer experience. “Machine learning acts as a medium to take potential customers through a precise, tailored, and dedicated experience that companies have been trying hard to create manually since years. Moreover, with the help of machine learning, the salesperson can be relieved of the daunting tasks of impressing the customers and their skills can be rather used to focus on consumer engagement and sales development,” says Agarwal of

It is proven that the ML can certainly transform any salesperson to a completely experienced engineer by just suggesting the right products and services to sell. ML eliminates the need for intensive training which is highly time-consuming and thus helps in Predictive Analysis.

In order to analyse and significantly reduce customer mix, ML has proved to be very fruitful in streamlining the various risk predictions. Businesses instead of completely relying on expensive methods to minimise customer churn must turn to ML.

“All the loans that we approve are stored in the system along with how they are performing. So automatically, this helps us reduce wrong underwriting calls taken by us in the past and the system gives us an alert when similar case is being approved which has gone bad. It is like an automatic recommendation system by Machine Learning which ensures good underwriting going forward,” says Rachit Chawla, CEO, Finway. These tools are still evolving and will help the finance companies minimise losses, indulge in smarter trading and give best of experience to the customers.

“OBOPAY uses Machine Learning for its products like Distribution Management System, which has an integrated payment platform and OBOPAY Cards. This intelligent engine helps us understand the user pattern to detect fraud and out-of-line transactions,” says Shailendra Naidu, CEO, OBOPAY.

For instance, if a digital account linked to OBOPAY Card that has been dormant for a very long period becomes hyperactive suddenly, the Machine Learning AI platform that the company uses would raise the red flag as the transaction looks suspicious.

Thanks to disruptive technologies like Artificial Intelligence (AI), one can now say bye to manual processes in the lending industry. The comparison of data, the verification process, credit assessment, the approval of the loan and the disbursal process can now all be done in lesser time. Also, human error is something that the use of AI helps in removing to a great extent. “With the current pace, it won’t be wrong to say that the entire credit assessment process would be taken over by artificial intelligence in the consequent years,” says Chopra of IndiaLends.

Through big data, is predicting claims ratios and accordingly profiling customers, thereby enabling efficient pricing of the end-product. Analytics also enables the company to identify the right time to reach out to the right customer, so that it attains a higher rate of conversion.

AI also helps in assessing the creditworthiness of a client using all the data points received manually and then cross-checking the same using AI across various platforms. Like an automated score of a borrower. “The use of AI is helping us in fraud detection and management. Sometimes AI red flags a regular transaction and we see it as normal, the system detects that and learns from the experience as to what is a fraud and what is not,” says Chawla of Finway.

With regards to Internet of Things (IoT), Chawla says, “Our APIs are connected with the APIs of banks, credit agencies, social media, agent app, RCU, government, agencies which fetch real-time data and analysis about the information of a borrower real-time. In seconds, our system talks to these multiple systems and fetches the desired data instantly. We understand the impact of IoT with around 100 billion connected IoT devices in the near future and by 2025 the global economic impact of this would be more than $11 trillion.”

According to recent industry reports, 156 million of Indians who comprise the ‘urban mass’ and urban middle’ section representing an annual income of $3000 and above have the potential of mass adoption of consumer credit. Of this, the ‘urban mass’ constituting approximately 129 million have been mostly deprived of credit due to lack of credit history. “In order to address this concern CASHe, a leading digital lending company developed India’s first credit rating system naming it SLQ – Social Loan Quotient. It’s a real-time platform which leverages big data analytics, Artificial Intelligence and predictive tools in order to arrive at a credit score of a specific person. It’s an AI engine which uses a proprietary algorithm to analyse wealth of data to find recurring patterns of credit behaviour that will indicate an individual’s willingness and ability to pay his financial obligations,” says Ketan Patel, Executive Director & CEO, CASHe.

It uses revolutionary approach links, multiple online and offline data points like individuals mobile, social and media footprint, education, remuneration, career and financial history to calculate the borrower’s credit score.

COCO by DHFL General Insurance is utilising AI, ML, and other new-age technologies to create hyper-customisable, multivariate customer journeys where the digital customer platform actively learns from user behaviour to serve unique communication as well as purchase experiences depending on the need of every individual customer. “Using technology, COCO puts the power of choice right into the palms of the customers,” says Vijay Sinha – MD & CEO of COCO by DHFL General Insurance.

Mumbai-based fintech company ePayLater while providing credit to customers goes through a process of risk assessment to estimate the creditworthiness of a prospect. ePayLater uses advanced ML techniques to do real-time credit assessment by leveraging data such as buying patterns with the merchants, digital footprint, social media information and device information, etc.

“ePayLater works with different merchant partners that cater to very different target groups and markets. IRCTC sees traffic from the biggest metros and the smallest towns and gets customers of different ages that often lack credit histories. In spite of these challenges, ePayLater’s algorithms have been able to determine their creditworthiness using alternate methods to gather and analyse the data available,” says Aurko Bhattacharya – Co-Founder ePayLater.

Leveraging technology is the key for Fintech companies. P2P lending company RupeeCircle is a strong believer in technology disrupting the lending market. “RupeeCircle has been using AI and machine learning for predictive analysis which helps in determining our proprietary RC score. For running predictive analysis, we have created a recurring neural network which can model patterns over time, in turn helping us in identifying the creditworthy borrowers. Similarly, cognitive computing is helping us make sense of 5000+ traditional and digital data points of the borrowers we collect,” says Ajit Kumar, Founder, RupeeCircle.

“Optical Character Recognition (OCR) is used to digitise hard copy documents. Machine learning model with layered business logic then interprets and record data without any manual intervention, thus reducing the scope of human error. Hence, our underwriting process has been successful to a great extent and has helped us maintain the NPA rate below 2%. Blockchain is another technology, we feel has a lot of potential. We are exploring smart contracts between borrowers and investors using blockchain. Also, we would like to explore blockchain to ease the KYC process for the customers by creating digital IDs for the customer,” he concludes.

( The author is a Delhi based writer & blogger on BFSI Industry.)


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