How new technology is changing the face of fraud detection in neobanks – FinTech Futures

In 2018, there were just 60 worldwide challenger banks in operation. Today, that number has skyrocketed to nearly 300, and as exciting as the new landscape is, the strain is beginning to show.

AI and ML can protect neobanks from fraud before it takes place

Digital banks are laser focused on creating fast, simple, frictionless customer experiences, from applications and approval processes to lightning-fast account access. Now, instead of timing how long it takes to fill out an application in terms of minutes, banks are measuring down to the clicks required to successfully apply for a product, shaving milliseconds off of their customer experiences to outdo their competitors.

Every step makes for an improved customer experience, but as these institutions are quickly discovering, that speed comes at a cost. As processes speed up and the volume of applications increases, fraudsters have slipped in, eager to exploit these platforms every way they can.

According to research from Aite-Novarica Group, fintechs like neobanks experience a fraud rate at an average of 0.30 percent. That’s twice as high as the credit card fraud rates of 0.15 to 0.20 percent and triple the debit card fraud rates of 0.10 percent.

Anyone who has dealt with organised criminals online can confirm that it can be hard to see fraud as it is happening and correct those losses once the fraud has already taken place. It’s even more difficult to prevent fraud before it happens. The stakes are high to address this problem—from reputational hits to financial losses, fraud takes a toll. Fortunately, there are tech solutions driven by artificial intelligence (AI) that can help banks stop criminals before they strike.

Scams take a toll on reputation

Competition is high, and so is the risk of fraud in a world where fast is better and customers have less patience for bulky and unwieldy application processes. What customers may not connect, though, is that the speed, if left unchecked, can welcome in criminals, putting their data at risk.

For the digital bank, whether the fraud happens directly to customers or criminals create false identities to defraud banks directly, it’s bad for business. Reputation is important, whether an institution is a century old or a year old, as many neobanks are. For those newer banks, though, the risk of losing clients is much greater, as individuals have fewer ties and less loyalty to a brand they’ve only been with for months or a year.

Further, this reputational harm extends to the overall viability of a digital bank to operate as hoped—due to the risk of data breaches and fraud, some merchants have stopped accepting digital bank cards altogether. This, of course, damages a fintech operation’s reputation and, with limited opportunities for use, drives customers away.

Between a lack of access to merchants and a lack of faith in a neobank’s ability to protect itself and its customers, the impact of fraud on reputation can put an end to a fintech organisation before it even really gets started.

The financial losses

Before the toll of losing customers comes into play, scams affect the bottom line with speed and immediacy. These losses are more familiar but no less distressing, and as application fraud continues to grow, it’s become clear technology as it stands has fallen far behind criminal operations.

The growth of digital channels for bank applications—now quicker and easier than ever before for customers—has created a risk-free way for fraudsters to apply for new accounts without being detected. Because these applications can take less than a minute, if a criminal’s application is denied, they can learn quickly from their mistakes and make attempt after attempt until they succeed. The losses here are direct, as criminals apply for and are approved for loans, only to default, taking the money and never to be heard from again.

Then there are the losses from identity theft and theft of customer information and funds, which fintech companies may have to pay out at a direct loss. Criminals have exploited these new, faster systems, but fintech companies can find a way forward with tech that can outpace fraud.

The right tech to prevent fintech fraud

AI and machine learning are the key to not just recovering from, but actually preventing fraud before it begins. Instead of spending valuable time and energy sorting through endless lines of siloed data out of context, technology like entity resolution and network generation can provide a full picture of a potential customer, putting risk in context.

In the case of lending fraud, for example, instead of detecting fraud after it’s happened, fintech institutions can use analytics technology to rate each online application for fraud risk, not just credit risk. Most banks, whether brand new or decades old, have more data than they know what to do with—AI can actually bring in more data to provide context for an applicant without overwhelming staff.

There’s far more data available for organisations to help root out fraud, from social media data to physical addresses and email accounts to phone numbers. When considering an applicant, even within faster timeframes than ever, the right technology can identify the risk, reveal unusual patterns of behavior and pinpoint anomalies that subtly indicate signs of fraud.

In other words, AI can protect neobanks from fraud before it takes place, cutting down financial losses and building and preserving their reputations when it’s most important—right at the start of their journey.

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How new technology is changing the face of fraud detection in neobanks – FinTech Futures

In 2018, there were just 60 worldwide challenger banks in operation. Today, that number has skyrocketed to nearly 300, and as exciting as the new landscape is, the strain is beginning to show.

AI and ML can protect neobanks from fraud before it takes place

Digital banks are laser focused on creating fast, simple, frictionless customer experiences, from applications and approval processes to lightning-fast account access. Now, instead of timing how long it takes to fill out an application in terms of minutes, banks are measuring down to the clicks required to successfully apply for a product, shaving milliseconds off of their customer experiences to outdo their competitors.

Every step makes for an improved customer experience, but as these institutions are quickly discovering, that speed comes at a cost. As processes speed up and the volume of applications increases, fraudsters have slipped in, eager to exploit these platforms every way they can.

According to research from Aite-Novarica Group, fintechs like neobanks experience a fraud rate at an average of 0.30 percent. That’s twice as high as the credit card fraud rates of 0.15 to 0.20 percent and triple the debit card fraud rates of 0.10 percent.

Anyone who has dealt with organised criminals online can confirm that it can be hard to see fraud as it is happening and correct those losses once the fraud has already taken place. It’s even more difficult to prevent fraud before it happens. The stakes are high to address this problem—from reputational hits to financial losses, fraud takes a toll. Fortunately, there are tech solutions driven by artificial intelligence (AI) that can help banks stop criminals before they strike.

Scams take a toll on reputation

Competition is high, and so is the risk of fraud in a world where fast is better and customers have less patience for bulky and unwieldy application processes. What customers may not connect, though, is that the speed, if left unchecked, can welcome in criminals, putting their data at risk.

For the digital bank, whether the fraud happens directly to customers or criminals create false identities to defraud banks directly, it’s bad for business. Reputation is important, whether an institution is a century old or a year old, as many neobanks are. For those newer banks, though, the risk of losing clients is much greater, as individuals have fewer ties and less loyalty to a brand they’ve only been with for months or a year.

Further, this reputational harm extends to the overall viability of a digital bank to operate as hoped—due to the risk of data breaches and fraud, some merchants have stopped accepting digital bank cards altogether. This, of course, damages a fintech operation’s reputation and, with limited opportunities for use, drives customers away.

Between a lack of access to merchants and a lack of faith in a neobank’s ability to protect itself and its customers, the impact of fraud on reputation can put an end to a fintech organisation before it even really gets started.

The financial losses

Before the toll of losing customers comes into play, scams affect the bottom line with speed and immediacy. These losses are more familiar but no less distressing, and as application fraud continues to grow, it’s become clear technology as it stands has fallen far behind criminal operations.

The growth of digital channels for bank applications—now quicker and easier than ever before for customers—has created a risk-free way for fraudsters to apply for new accounts without being detected. Because these applications can take less than a minute, if a criminal’s application is denied, they can learn quickly from their mistakes and make attempt after attempt until they succeed. The losses here are direct, as criminals apply for and are approved for loans, only to default, taking the money and never to be heard from again.

Then there are the losses from identity theft and theft of customer information and funds, which fintech companies may have to pay out at a direct loss. Criminals have exploited these new, faster systems, but fintech companies can find a way forward with tech that can outpace fraud.

The right tech to prevent fintech fraud

AI and machine learning are the key to not just recovering from, but actually preventing fraud before it begins. Instead of spending valuable time and energy sorting through endless lines of siloed data out of context, technology like entity resolution and network generation can provide a full picture of a potential customer, putting risk in context.

In the case of lending fraud, for example, instead of detecting fraud after it’s happened, fintech institutions can use analytics technology to rate each online application for fraud risk, not just credit risk. Most banks, whether brand new or decades old, have more data than they know what to do with—AI can actually bring in more data to provide context for an applicant without overwhelming staff.

There’s far more data available for organisations to help root out fraud, from social media data to physical addresses and email accounts to phone numbers. When considering an applicant, even within faster timeframes than ever, the right technology can identify the risk, reveal unusual patterns of behavior and pinpoint anomalies that subtly indicate signs of fraud.

In other words, AI can protect neobanks from fraud before it takes place, cutting down financial losses and building and preserving their reputations when it’s most important—right at the start of their journey.

Spread the love

Leave a Reply

Your email address will not be published.