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How Effectiv Plans To Help Banks And Credit Unions Fight Fraud


With the banking industry losing an estimated $19 billion a year to fraud, investing in technology that can bring those losses down is a no-brainer. But for mid-sized banks, credit unions, fintechs and other smaller institutions, the most sophisticated anti-fraud technology is often just too expensive. Enter Effectiv, a US start-up that aims to put that right – and which is today announcing a $4 million seed funding round.

Effectiv’s founders are anti-fraud technology veterans who got to know at each other at Simility, a specialist in the industry that was acquired by PayPal in 2018. Last year, they stepped away from their former employer to launch a business aimed at small and medium-sized financial institutions. “It’s a massively underserved area of the market,” says Effectiv co-founder and CEO Ravi Sandepudi.

In the US alone, Effectiv estimates its target market has customer relationships with around two-thirds of the population. It is also targeting similar-sized banks and financial institutions in Canada, and in the UK, where the country’s building societies would be an obvious market.

The idea is to harness advances in technology to give these smaller players access to the same fraud detection techniques as the largest banks. In particular, Sandepudi points to cloud computing and machine learning as the two enabling technologies that make Effectiv possible.

“A decade ago, you could only run these kind of technologies as on-premise installations, which was time-consuming to set up and expensive,” he explains. “Now you can run them as a service in the cloud.”

As for machine learning, this is the technology at the core of Effectiv’s solution. In essence, the company is selling a machine learning engine that looks at customers’ historic transactions in order to understand what is their typical behaviour – what kind of transactions do they undertake, when and where, for example? Having developed a picture of how a customer behaves, the software can then flag up anomalous transactions that don’t fit the picture.

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Not all such anomalies will be fraud, of course. Customers sometimes do different things that are entirely legitimate. But Effectiv’s customers can set the software to respond to anomalies in different ways. Some will be regarded as more serious – the biggest and most anomalous transactions, say – and merit an intervention; that could be anything from a phone call from customer services to a full-scale freeze of the account. Others might just be noted – if they don’t turn out to be a fraud, the machine learning engine will learn from the experience and adjust its expectations.

In practice, it’s the customer who decides when interventions are necessary. “Think of it as being like a dial that you can turn up or down according to your risk appetite,” says Sandepui. Interventions cause friction for the customer, he points out, so there is a balance to strike. Customers don’t want to have to unfreeze their accounts each time they spend in a different place, or change their habits; equally, they don’t want to fall victim to a fraud.

This balance is particularly hard to get right at smaller institutions and credit unions where relationships of trust with members and customers are incredibly important and depend on experience, says Sandepui. “It is hard for them to adopt technologies that can potentially hamper that experience by adding undue friction,” he says. “We have worked very hard to build that trust in the last few months by working with only a few institutions but ensuring our platform performs great for them.”

The customisation of Effectiv’s software in this way has to be something customers can manage without specialist technical skills, adds Ritesh Arora, co-founder and president of the company. “We are building a platform that is designed from the ground up to be extremely easy to integrate and implement,” he explains. “Our goal is to help teams manage their risk strategies without relying on developers.”

It is early days for the company, which took on its first customer last summer but only began actively marketing its services to customers earlier this year. Nevertheless, investors are impressed with the concept and keen to back the business: the $4 million seed funding round announced today is led by the venture capital giant Accel and backed by investors including REV.

“As the world is on a digitisation spree, we know that challenging times are ahead for financial institutions,” says Dinesh Katiyar, a partner at Accel. “We see tremendous value in Effectiv’s innovative approach to fraud and risk management. Their powerful yet rapidly adaptable artificial intelligence platform could be the key to identifying these changing trends.”



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