Power more loans with new-age credit underwriting

Pankaj Kulshreshtha and Pankaj Jain founded Scienaptic in 2014 to replace legacy underwriting systems with agile, tech-driven credit decisioning. Noting that borrowers rarely fit into neat profiles and FICO scores, the co-founders saw a need for risk assessment methods that were better attuned to today’s complex markets. They were also determined to provide financial access and inclusion to unbanked, underbanked, underserved and overlooked communities.

Chandan Pal, chief marketing officer at Scienaptic, stated that the company has remained steadfast to its founding principles. “Scienaptic's motto has always been to transform lives, both for our clients and the customers they serve,” he said. “Scienaptic wants to help clients make better credit decisioning so they can say yes to more borrowers, more often.”

Pal noted that Kulshreshtha has more than 20 years of experience in analytics, financial services and risk management, including GE Money UK, where he served as chief revenue officer and created a multi-industry analytics business. Today, he and a team of 100 specialists help financial institutions and specialty lenders tackle issues of credit expansion and availability across the globe through AI and machine learning solutions. 

AI-powered platform

According to Pal, financial institutions need to be more inclusive in lending practices, and Scienaptic helps them achieve this through better credit decisioning and improved access to credit, without increasing lenders’ risk. The platform uses AI/ML models to give sharper credit scores and mitigate potential risk, he added.

Lenders can leverage Scienaptic’s smart suite of AI underwriting tools, which combines traditional with alternative credit data through pre-built APIs to enhance and accelerate data ingestion, Pal stated. This plug-and-play approach enables lenders to derive a fully dimensional credit profile while quickly identifying qualified prospects and flagging potentially high-risk prospects, he explained.

Designed to help lenders build stronger customer relationships and capture greater wallet share, Scienaptic’s AI platform has scored high with participating clients and partners, and it augments and extends the value of their existing investments, Pal stated. For example, he added, a week after implementing the platform, a financial institution saw credit approvals jump from 61 to 81 percent.

Expanding, scaling credit access

Scienaptic has delivered $4 billion in incremental loans for financial institutions since its inception, saving lenders over $200 million in credit losses, the company reported. Currently, Scienaptic added, the platform serves lenders with assets totaling more than $100 billion and processing nearly seven million loan applications annually, worth over $22 billion. On average, participating financial institutions are able to increase loan approvals by approximately $150-200 million using the platform, according to recent reports.

Lenders can use Scienaptic’s bolt-on solution to navigate and originate new loans with confidence, Pal stated, pointing out that the platform gives clients the agility to react to market changes while testing and deploying new models and rules. A recent case in point, buy now, pay later, became popular during the pandemic, when consumers wanted to split purchase costs over time, he added.

“Scienaptic’s AI-powered credit decisioning platform provides BNPL fintechs with underwriting tools that help them better identify qualified leads and high-risk prospects,” Pal said. “In a recent implementation, the platform delivered $100 million in incremental sales for BNPL customers for a leading online retailer.”

Pal went on to say that in the coming months, Scienaptic will continue to scale its subscription service model to facilitate easier decisioning at a global level. This will allow lenders of all sizes across the world to transform their lending capabilities in just a few clicks, he said, while ensuring that more customers get loans and lending risks are drastically reduced in the process.

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