Scienaptic is a big data and analytics startup that helps banks and financial institutions score and understand their customers better.
At a glance:
Based out of: New York
Founders: Pankaj Kulshreshtha
Sector: Data and Analytics
Problem it solves: Bank credit risk analysis
When Pankaj Kulshreshtha decided in 2014 that he wanted to start up, he knew it had to be in the big data and analytics space. Having been part of the sector since before Big Data, Analytics and Machine Learning became catchphrases, Pankaj knew there were big gaps that needed to be addressed.
One area he saw was banking and its customer experience journey. Even though there are numerous new ways to get customer information digitally, it has not been easy to evolve banking systems to leverage all this data using Machine Learning. Banks have struggled with embedding predictive intelligence in customer interactions.
Also, these processes often reduce the satisfaction level of a customer – and the irony here is that banks spend huge amounts of money on data analytics and machine learning software to set up these very systems.
“We decided to build a plug-and-play product that works with the bank systems and provides signals that make processes like credit underwriting, credit line management, smart fraud management, collections, cross-selling and targeting customers smarter and quicker,” says Pankaj
Scienaptic primarily works with banks and financial services institutions, and its core competency is retail banking.
The key workings
Most large and medium scale banks have existing data infrastructure, and Scienaptic’s analytical platform ‘Ether’ takes up this infrastructure to deploy its software.
Its Machine Learning engines determine the kind of customer each one is, the best time to make promotional calls to them, and what products they are most likely to buy, as also how the customer needs to be dealt with. This also means a customer need not go through endless IVR sessions and once he or she dials in, the platform can intuitively anticipate requirements and present relevant information immediately.
What Ether does is provide end-to-end solutions powered by Machine Learning and Artificial Intelligence. The core focus are on credit decisioning and fraud management. The team claims that the Machine Learning driven approach populates a multi-dimensional “Customer Consciousness” that can evolve to provide intelligent signals to other processes like customer service and collections.
“The different signals are generated by our product and are directed into the workflow solutions that most of these banks have. Our core competency is our ability to understand business process banking, which is differentiated by this mechanism where the data is taken from the existing infrastructure,” says Pankaj.
He adds the product is built to make the platform fluid and has the ability to address any need of a banking client. He explains the platform can also highlight data inconsistencies and issues, and work within the framework of the existing infrastructure.
The data is analysed by the platform and is presented to the client bank in a simple and easy-to-understand format. Scienaptic analyses the usage patterns of a customer and scores their creditworthiness accordingly. The typical charges are $1 per customer. They also have an enterprise option.
Challenges and market
The task of building the platform, however, wasn’t simple. While Pankaj had the analytics background, he needed the key technologists. In the initial days, he used his contacts and hired freshers. But hiring tech talent continues to be the single biggest challenge.
“Also, within a few months of starting up, I had a co-founder who left. I had to make the call of running this as the sole founder. Since then I have been careful about hiring senior talent,” explains Pankaj.
The need for data analytics is fast growing and while most acknowledge its importance, over the last few months, there has been a growing debate on big data and privacy. Facebook has come under fire for sharing data with Cambridge Analytica. The latter, a data analytics firm, is said to have used data from over 50 million Facebook users without their consent to allegedly manipulate US Presidential elections.
In India, the market for data analytics is believed to be growing at a compounded annual growth rate of 23.8 percent. There are companies like UK-based Aire, US-based Kabbage, Kasisto, and several others who are tapping into this space of AI in banking.
“There is a big use of data analytics in banking sector, especially in risk analysis. The top 20 retail banks in the US write off close to $50 billion in credit losses. If we can help that improve by even 10 percent, then it will change the way credit is scored. We believe that we have solutions that can reduce the losses by 20-30 percent,” says Pankaj.
While the team declined to mention the names of its client banks, it mentioned it works with marquee banks in the US and two major financial institutions in India.
The team aims to deepen its presence across US and India and work on its products.