Datanomers
The Next Frontier

Underwriting is one of the most complex, manually-intensive processes, which imposes a lot of inefficiencies and expense for the organization. It involves underwriters to search public domains on the internet to check for information about applicant’s credit risk. This unstructured information is then analyzed manually for risk assessment. Considering the fact that this manual process is time-consuming, there are high chances that underwriting can become cost-prohibitive and inconsistent, approving high-risk loans. IPsoft, an AI company specializing in Natural Language Processing (NLP), spun off Datanomers to develop FinTech solutions for commercial underwriting. Datanomers’ Financial Risk Profiler (FRP) couples Machine Learning with NLP to automate the sales and underwriting of commercial loans. In this interview, Deepak Dube, CEO, Datanomers, along with Pranay Pogde, President of Engineering, Brian Lemelman, Advisory Board Member, and Meeta Pandey, Vice President Business Development share some insights about their organization and the key functionalities of their offering.

What sense do you get of the challenges that CIOs face now in the FinTech Solution space and how is Datanomers effectively addressing these issues?

The first and the most predominant challenge in the FinTech space is the aging legacy systems. Many companies struggle to modify, upgrade, or replace them to stay on par with growing competition from nimble start-ups with AI capabilities. Datanomers’ solutions are easy to “bolt-on” to legacy systems enabling our clients to become an AI-enabled organization quickly. Secondly, it is the understanding that IT is a partner that can not only save costs by adding efficiencies but can also add significant value to the top line growth of the company. Today, CIOs are partners to the business in the top-line growth with commitments to deliver P&Ls. Another big challenge is the lack of data to train the AI machine. Datanomers’ proprietary self-calibrating machine overcomes this challenge where, starting with minimal training data, its predictions become more accurate with time. This is especially applicable to the FinTech space, where the economic and financial conditions are dynamic.

Please walk us through your FinTech Solution on the basis of its methodology, features, and benefits involved.

Clients use FRP to mine the Internet, extract relevant information to build a credit risk profile for an entity, combine it with financials, score the larger data set of financials and leading indicator information from the internet, and finally use the score to make commercial lending decisions.

This eliminates costs through the automation of the underwriting process, reducing defaults, lending profitably to those who otherwise might have been rejected. In other words, it grows top-line by lending more, while reducing expenses by automation and reduction of defaults, and helps grow the bottom-line also.

Automation of underwriting for commercial loans or insurance

Identifying and qualifying lead opportunities and then underwriting loan applicants, is a tedious process of gathering information, processing applications, asking for more information where required, and reviewing it again, to make a decision. There are several FRP products - two of which are FRP-Productivity and FRP-Predictive. FRP-Productivity helps automate this tedious process partially by collecting relevant information automatically from public (the Internet) and private (the intranet) sources. This reduces the underwriting expense by about 10-15 percent, which is a significant saving. FRP-Predictive’s machine learning analyzes applicant’s financials and Internet data to predict loan outcomes to reduce your defaults 5-15 percent.

Better loan decisions

Banks and non-banks lend trillions of dollars annually and can sustain losses due to default and fraud as high as 10 percent or more. Not only do these losses impact the bottom-line directly, but lenders are also required to tie up precious capital in loss reserves. What if this tremendous drain on business could be lifted? FRP Predictive uses conventional financials plus internet data to make better decisions about lending to healthy commercial entities. Typically, it reduces defaults by 5-15 percent, which potentially amounts to hundreds of billions of dollars globally.

Could you please cite one or two case studies on how you have enabled clients to overcome hurdles and attain desired outcomes with your innovative solution?

Banks—big and small—and insurance majors have benefitted immensely from FRP’s FinTech ecosystem. For example, a medium-sized bank, headquartered in NYC, uses FRP’s scores to help make better lending decisions. Datanomers adapts its solutions to fit a customer’s process so that the CIO of the organization doesn’t have to spend a lot of effort in integration. This was a big advantage for this customer. We engineered the solution to fit at the tail-end of the process, where FRP-Predictive’s expert guidance on loan outcome helped the company lower their default rates. The test and turn-up process was so seamless that this customer is exploring other AI solutions from our portfolio. A Canadian bank, with a large North American footprint, is deploying FRP-Productivity for automating the underwriting process.

What does the future hold for your organization? Any footprint expansion plans or platform enhancement strategies that you can shed light upon?

Datanomers is a Technology company with innovative FinTech Applications. We constantly think about applying our technology to other use cases and industries to solve problems. For example, we are working with two major commercial insurance companies to automate risk assessment. Through our NLP technology, they have streamlined and simplified risk assessment by merely uploading survey risk reports, which is a large document about 100-150 pages.

Our Insurance Risk Profiler identifies and highlights key information about the financial risks associated with an applicant, helping the underwriters determine and establish a risk profile for them. It also predicts whether a company should insure the applicant or not. This helps them with significant productivity gains and cost savings through automation.