AI and ML technologies diversify the lending ecosystem seamlessly, efficiently, and effectively. The digitalized world we live in has enabled individuals and businesses to grow and keep ahead of their competition. Many mobile lending apps have exploded in India in recent years due to the increasing accessibility of smartphones. The government encouraged digitization in banking which resulted in financial technology (Fintech), firms racing to fill the gaps, especially in the category of digital loans. Disruptive technologies such as Artificial Intelligence and Machine Learning are gaining popularity in nearly every industry. The financial sector is also a beneficiary of large amounts of data.

The current issues of the lending industry can be solved through technology and digitalizing the operations as much as possible. A thorough research and analyses on this matter has led Rishabh Goel to the idea of launching Credgenics, a technological solution to digitize a largely manual collections workflow. Rishabh was soon joined by Anand and Mayank, who are currently the CTO and COO of Credgenics respectively. Analytics Insight has engaged in an exclusive interview with Rishabh to discuss about his vision of creating a technology-based solution for the lending industry. After graduating from IIT Delhi, I worked first with Deutsche Bank and then with Blackrock, where I understood the nuances of the lending industry and observed the problems with the current collections practices.

An important consideration for data scientists, businesses, and society as a whole, today centres on how we might establish AI as an indisputable and indispensable force for good in the world. For years, we have seen stories of bots and machines taking over the job market, discriminatory facial recognition technology, and few of us will forget the turn of events with Tay. In most instances where AI has fallen foul, however, the appropriate response is simple and follows a long tradition of noble scientific endeavour. Quite simply, how do we build it better? In every context, better thought aroung data and models leads to improved products and services. When machine learning models for cancer diagnoses show promise, we naturally rally around this positive step and rejoice in the vision of a brighter future because it's a victory that touches us all in some way.

Consider the massive size of real estate lending. The Fed's latest report shows mortgage debt topping $9 trillion. When including mortgages from businesses, it tops $15 trillion. Over 10 million homes and commercial properties sell each year. Equally staggering is how much data exists on the borrowers.

Over the year, evolution in global technologies has made people move forward from fixed phones to mobile phones. Today, every sector is readily and rapidly adapting the Artificial Intelligence (AI). With the fast-paced modern industries, AI is becoming an integral part of business operations. AI is not limited trend-based forecasting in marketing but its presence is getting indispensable in every vertical of the company. AI is much more efficient in analysing data patterns, based on these patterns companies acquire in-depth knowledge about their potential customers, their requirements and their behaviour.

Even though the banking industry has changed drastically, there are still leaps and bounds of improvements to be implemented across the board. The simultaneous processing of different algorithms to establish patterns in consumer behaviours and the ability of computers to extract data from documents to answer questions will further minimise the input required from humans to complete a range of banking transactions. Instead of focusing on form completion and transferring data, mortgage underwriters will be available to spend more time examining loan applications and credit assessments at a higher level. Ultimately, this means that decisions to extend credit will involve less risk and be available to more worthy borrowers.

With self-driving cars cruising around, robots doing backflips and helping each other open doors, computers learning how to play GO in a few days and then beating experts who spent their lives mastering the game, we are definitely witnessing an exciting era in human history. Like Enova's CTO John Higginson said in his blog post, as an analytics and technology company we want to use and even seek to extend these technologies to invent the future, but for credit. That's exactly why our executive team picked'advancing our machine learning capabilities' as one of our strategic initiatives this year. Usually when people see these amazing advancements in technology the first thing they think about is how machines are taking over our jobs. In the lending industry, however, the takeover has already happened.

So, how AI can be used to re-think the lending industry? AI goes much beyond the binary world of lend-don't lend decisions. It can improve customer personalization, identify patterns and connections that humans can't, and answer questions in real-time. Historically, lenders used to make go-no go loan decisions based on a loan applicant's credit score. Digital lending platforms believe that this kind of information does not paint a complete picture of a loan applicant's creditworthiness.