Financial crime is on the increase and attackers must be stopped in their tracks. Financial service organizations new and old need to ensure they have the right technology in place to predict, detect and deter fraud, whilst ensuring minimal disruption to the customer journey.
We spoke to Martin Rehak, CEO of Resistant AI to find out how sophisticated artificial intelligence can detect known criminal practices and more importantly, predict the unknown emerging patterns of financial crime.
BN: What are the trends you are currently seeing in financial crime?
MR: Post Brexit, with opportunities opening up for the UK to take advantage of new-found regulatory freedom, the financial services industry is seeing rising figures from organized fraud. A recent report from Cifas, the UK’s fraud prevention community, found that during the first six months of 2021 there was an 11 percent increase in incidents of identity fraud relative to the same period in 2020. The same report also pointed to the rise in cybercrime as a service — phishing kits, fraud tool kits and hacking services — all posing extremely high threats to all sectors.
The fraudsters targeting businesses and consumers alike are deploying ever more sophisticated tactics and it’s becoming increasingly difficult for cyber security teams to keep up. Today’s attackers are clever, constantly changing their attack methodology and identities and, as a result, causing considerable damage.
We are seeing a particular rise in the following areas:
- Account takeover — a form of identity theft and fraud, where a malicious third party successfully gains access to a user’s account credentials.
- Synthetic/sign up fraud — in the case a criminal combines stolen data with false information to create a new identity.
- Money mule — a person who transfers money acquired illegally in person, through a courier service, or electronically, on behalf of others.
BN: Do you think there has been a rise in financial fraud as a result of increased online activity during the pandemic and new payment methods such as deferred payment?
MR: Absolutely, and COVID-19 has a lot to answer for that. Over the past 12-18 months, we’ve seen an increased online presence for many businesses, widespread remote working and a surge in new payment options, such as Buy Now Pay Later. The appeal of this new type of easy credit is clear: it enables shoppers to get their goods immediately, but part with their money more slowly, paying either interest-free monthly installments or a lump sum. Klarna is now one of Europe’s biggest fintechs, valued at £33bn ($44bn), with more than 90 million customers. In addition, retail outlets who only ever had a high street presence have moved online, often completing a transaction by just requesting an email address rather than any formal identity verification.
BN: What role do you see AI playing in detecting these crimes and how does it work?
MR: First, it has to be said that without AI it will be virtually impossible to defeat these fraudsters who are themselves using AI. I think we must accept that fraudsters are often one step ahead, but at least if both sides are using AI, then it’s a more level playing field.
Sophisticated AI is able to predict, detect and deter financial crime. Continual assessment of transactions, customer behavior within a session, across sessions and between sessions may alert teams to fraudulent activity taking place in the moment, or highlight anomalies which could point to a crime that’s taken place in recent weeks, months or even years. These anomalies could be behavioral, device characteristics, relating to Internet and/or financial service providers, contact information, geo-locations, spikes of related activity, unusual switching between accounts, the list goes on… Efficient and effective anomaly detection identifies behaviors that deviate from the expected and which might be symptomatic of criminal activity. AI can block attacks, and push criminals to the sheer limit of their efficiency.
BN: Do you feel there is still a place for human intervention in the fraud prevention process?
MR: The combination of AI, automation and the human brain is the strongest form of defense when it comes to fighting cybercrime. It’s well known how overwhelmed many fraud (and cyber) analyst teams are with an ever-increasing number of alerts — many of them false.
Automation comes into play by narrowing the focus of investigations. With the priority alerts identified, fraud analysts then receive a complete view of transactions taking into consideration historical data, real-time analytics and insight when assessing risk.
BN: If you need to increase security to combat cyber criminals, won’t this make the customer journey more labored?
MR: The key challenge that fintechs and retailers alike face is to ensure the on-boarding process for new customers is as seamless as possible. In many cases, this has left businesses and their customers exposed to fraudulent behavior, some of which is unlikely to get spotted for weeks or months after the attack has taken place. In addition, false positives, where a legitimate transaction is flagged as suspicious, can be extremely frustrating for customers and can result in them not returning.
Utilizing AI to strengthen the validation, verification and transaction processes ensures security is enhanced — but not at the expense of the customer journey. Not only will this create a safer and more trusted customer experience it will play a key part in improving company reputation and attracting new customers.
BN: How can organizations ensure they are protected against financial fraud not only now but in the future, as the criminals get more clever and new technologies emerge?
MR: Financial crime will always be with us. The challenge is to find it as it shifts its methods and changes its targets. Traditional, rules-based countermeasures are simply not up to the task. Capabilities are required that can detect known criminal practices and recognize never-seen-before emerging patterns of financial crime. And this is where AI applied in the right way, comes into its own. By working with the right expertise, exploiting advances in cutting-edge AI, it’s possible to spot different types of threats or attack vectors and respond to them before it’s too late.
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