Taking the Risk out of Risk Analysis - An AI Story

Artificial intelligence (AI) has been celebrated for its application across a ton of fields, from medicine to agriculture, science, computer programming, finance and security. For the 21st century farmer, AI can help detect crop disease, predict crop yields and spray pesticide with pinpoint accuracy. With respect to medicine and health, AI can allow scientists to analyse the genetic code of DNA to detect genomic condition, predict hypoglycemic events hours in advance of them occurring, and detect cancer in tissue slides better than human epidemiologists. AI is also a key driver transforming the banking industry; helping banks save time, boost their revenue and bringing more accessible technology to consumers. 

But there is one area of banking where little recognition has been given until now of the immense opportunity AI offers: that of making risk analysis easier than ever before, essentially, taking the risk out of risk analysis.

Lauded a ‘gamechanger’ for risk management in finance, AI is providing banks and credit unions with the AI solutions and tools to identify potential investment risks as well as fraud - a considerable development considering that merchants lose on average 1.5 percent of their annual revenue through fraud. Risk analysis platform DeepRisk.AI is one of the game-changing platforms that is “taking away the risk from investments made by banks” according to DeepRisk.AI co-founder Chaitanya Vaidya, a US-based Software Engineer and Director of Technology at SignaPay.

The online platform is the very first end-to-end risk management platform for banks, powered by patented state of the art artificial intelligence technology. Founded by Vaidya, the FinTech startup helps risk analysts make the right decision, through enabling them to make simple inputs which then produce in-depth, easy-to-read risk analysis reports. Up until now, the financial services industry’s risk analysis approach was largely based on simple heuristics, with customer intelligence gleaned mostly through focus groups and consumer behaviour surveys - which left a lot of room for error. 

DeepRisk.AI’s AI software, however, transforms underwriting and real time transaction data into intuitive text insights, portfolio analytics and reporting almost immediately. 

“We developed DeepRisk.AI to help risk analysts at banks make better decisions with ease,” Vaidya said, before adding that DeepRisk.AI will launch its latest add-on service in mid-October of this year. Called the ‘Transaction Analysis Piece’ by DeepRisk.AI’s team, the service will help provide users with daily reports on investments they are considering making. 

“For instance, if someone wanted to know the risks of investing in a coffee shop in a small town in Texas that was showing a large annual profit margin, the DeepRisk.ai software would take into account all of the analytics of not just that coffee shop, but all the businesses in the surrounding area, to produce a result that would not only state that it is a high-risk investment but would also list out the factors as to why it is a high risk,” he stated.

“With the Transactional Analysis Piece, DeepRisk will tell our customers every day which transactions are of the highest risk to them, without them having to do anything,” said Vaidya. If the company’s success to date is anything to go by, the add-on service is sure to appeal to DeepRisk.AI users.

Having initially developed DeepRisk.AI to solve the problems faced by risk analysts every day, as opposed to building a business that would purely leverage the co-founders’ AI expertise, DeepRisk.AI took off at lightning speed as the world’s biggest banks quickly realised the potential it offered to risk analysis operations. 

“It was actually Founders Network that deserves some of the credit for how DeepRisk began,” the tech innovator and entrepreneur said, before explaining that it was at the Founders Network events that he met other tech-based entrepreneurs and developed his entrepreneurial skills. 

“The CEO of Founders Network, Kevin Holmes, was also so impressed with my ideas that he immediately told me to patent it, because I was onto something big,” Vaidya said. “So, I pitched it to my boss, and suggested spinning it out into a different company. And DeepRisk was born.”

Already, DeepRisk.AI has processed over three thousand merchants and is even being considered by some of the world’s biggest banks. And it makes sense. AI and risk management are perfectly aligned, with AI allowing risk managers of financial institutions far more structured analysis than their approach prior to the digital revolution: where countless hours were spent handling and evaluating unstructured data. AI solutions today can provide financial institutions with immediate, trustworthy data on customer intelligence, helping them make advised decisions, quicker.

The timing is fortuitous for DeepRisk’s founders. Risk management is serious business - one that includes over 30,000 companies and generates $6 billion in revenue every year. Leveraging AI expertise to tap into the FinTech world was a somewhat risky leap, but the risk has certainly paid off.