How Fraud Detection Using AI in Banking Flips the Board
Mitigating the risk of fraud at banks is an ever-increasing challenge. As technology has advanced at such a rapid pace, cybercriminals have become more sophisticated with their attacks. On top of that, the fusing of cryptocurrencies with the global economy has made combating fraud as well as money laundering even more of a daunting task. Thankfully, fraud detection using AI in banking is on the rise and has shown promise in this fight.
The reality is that the cost of fraud in banks and other financial services firms have increased over the years. Between 2017 and 2018, the level of fraud occurring at large financial firms as a percentage of revenue increased from 0.95% to 1.53%, which is a 61% increase. Unfortunately, these costs are only expected to increase, especially for larger firms.
Artificial intelligence has found a home in banking as the need for scalable improvements in customer service, anti-money laundering efforts, and investment decision making has become a necessity. As the digital transformation of banks, as well as the advancements of more sophisticated AI, continues, launching efforts into using AI for fraud detection can cut costs dramatically leading to improvements in customer experience, the bottom line, and a firm’s reputation.
Through the integration of AI as an advanced cybersecurity solution, banks are already able to reduce fraudulent activities occurring throughout their network by analyzing payments, loans, and customer onboarding. The amount of data involved, as well as their inter-connectedness, presents a massive challenge for banks to detect fraudulent activity.
Detecting Anomalies in an Ocean of Data
When you have a lot of data to analyze, it can be a major challenge to discern the signal from the noise and discover important anomalies. As the information age pushes forward, the challenge is no longer having enough data but how well we’re able to interpret its underlying meaning. With AI, we’ve been able to tackle this big data problem since AI algorithms usually work best in this type of environment.
More sophisticated AI algorithms, including deep neural networks (DNNs), are perfect for tackling fraud detection in banking as the amount of data is massive and can contain a lot of noise (or nuanced information). By employing a well-trained AI agent, banks have a better chance of finding very subtle anomalies in their network’s activities.
There’s certainly a good number of areas where insightful data can reside within a bank. However, when detecting fraudulent behavior there tends to be a few that stand out above the rest, especially payments, loans, and customer onboarding.
The number of transactions that occur within a bank’s network can be massive, which is why looking at payments can provide valuable insight into potential fraud. Similar to detecting anomalous transactional behavior for fighting money laundering, the same methods, including natural language generation, can be used to detect banking fraud.
Whether it’s a personal loan, credit card loan, home equity loan, or small business loan, cybercriminals have become more sophisticated at applying and receiving the most common types of loans banks provide. While banks want to keep their false-positive ratio as low as possible, it can be dangerous flirting with that line. Unfortunately, excess false positives can be just as costly since turning away honest customers results in a bad customer experience and leaving money on the table.
Keeping a good rate of new banking customers has a direct effect on increased revenue, but that’s only the case when the customers are legitimate. Once a cybercriminal can open a fraudulent account with a bank, they have immediate access to many of the bank’s services, including investments and loans. Detecting fraud from the start during customer onboarding is probably the best place to start since it’ll remove the possibility of any further fraud from occurring downstream.
One of the biggest challenges in fraud detection using AI in banking isn’t finding the data or even the right technology. The real challenge stems from convincing firms to embrace experimentation and innovation with new technology. Of course, banks need to be cautious due to the amount of responsibility they have, but firms that take even baby steps toward using AI to detect fraudulent behavior will reap the benefits in the long run.
Detecting banking fraud with AI can have a huge impact on the bottom line for banks and financial services firms due to their ability to better detect anomalous fraudulent behavior with a better false-positive ratio. By reducing false positives, firms are less likely to exclude legitimate customers resulting in leaving less money on the table.
Reducing False Positives Using AI
False positives are among the most costly results of running a bank since you’re effectively excluding a percentage of the market that legitimately wants to pay for your services. Imagine turning away customers en masse due to fears of them being fraudulent when in reality they’re honest customers.
In a case study by Teradata, they worked with a bank to reduce false positives by detecting fraudulent behavior using AI by a whopping 60% and they predict that number will increase to 80% as the AI continues to learn. In conjunction, they were also able to increase the detection of real fraud by 50%.
As more banks look to use AI as a way to effectively combat banking fraud, these numbers will only improve. As a matter of fact, Mastercard has implemented AI to monitor transactions within their credit card network to help reduce false declines by 80%, which is a major advancement when it comes to fraud detection using AI in banking.
Through the use of AI, monitoring real-time data to automate specific actions, such as accepting or declining a line of credit, can help many teams throughout the banking world to better leverage their skills and expert experience. Since AI also can produce predictive analytics, these very same teams can work alongside AI to amplify the efforts taken to reduce banking fraud and false positives.
Fusing AI and Banking Experts
Many people are afraid when they hear AI is entering their workplace because they might be under the impression that it’s there to take their jobs. However, the most successful companies are those that fuse the power of AI with the expert skills and knowledge of their employees.
Through this combination of forces, intelligence amplification (IA) emerges and can boost a business’s competitive advantage. When it comes to banks and other financial services firms, teams in charge of risk management and compliance can use all the help they can get.
Detecting fraudulent behavior within a bank’s network is becoming more and more challenging due to the increased technological sophistication of cybercriminals. By leveraging the power of AI, banks can automatically mitigate the risk of fraud more effectively while also freeing up their resources for more value-added tasks.
Artificial intelligence in banking is here to stay and that’s been proven with its ever-increasing adoption by top financial firms. Increasing the customer experience, reputation, and bottom line is a challenge every bank faces. Thankfully, even the most advanced AI algorithms are easily accessible, which allows banks of all sizes to take advantage of the many benefits AI has to offer.