How Can Machine Learning Help Us Keep Fraudsters away?
The drive to digital
In the first months of 2021, we view a boom in digital services, while the traditional physical economy has slowed to a crawl. To stay in business, a lot of companies are now being instructed to move services online faster than they decided. In the rush to get these new digital services to market, there is a significant risk that development teams can make mistakes and overlook the usual security checks. Unfortunately, the likely result is that fraudsters will have a field day as they find and exploit these new gaps in their victims' armour.
1. Keeping ahead of fraudsters
In a very dynamic environment where fraudsters are discovering new attack vectors every day, it's vital for fraud prevention teams so that you can detect threats and respond quickly. Artificial intelligence and machine learning approaches might help by spotting patterns in the past fraud cases and using these to detect suspicious behaviour by customers, employees or systems.
AI and machine learning are vast and highly technical fields, and it can be difficult for fraud teams to find the the easy way start their adoption journey. Nevertheless, banks and other organisations are placing a number of interesting AI/ML-powered anti-fraud solutions into production. For instance:
2. Facial and image recognition
Digital banks for example Monzo are utilizing smartphone cameras with facial recognition technology to avoid unauthorised users from gaining access to customers' accounts via their mobile apps. Today's powerful facial recognition solutions are made using machine learning models that can tell the difference between a customer's face and a photo or mask. They can even detect whenever a person is sleeping or unaware that the camera is being used, potentially which makes them a much more powerful access control measure than traditional password-based login methods.
Banks are also using image recognition to streamline processes for example paying in cheques, where customers take a photograph of the cheque and upload it via their banking app. Banks already use machine learning models to recognize whether the image is a genuine cheque and extract the important thing information from it. It will be a natural progression to analyse signatures and detect more kinds of potential cheque fraud.
3. Identifying suspicious behaviour
Natural language processing and text analytics can help companies handle larger volumes of internal and external communications – for example telephone calls, emails, SMS and instant messenger/chatbot interactions – while still maintaining robust anti-fraud measures. For example, in a banking context, many institutions already record the telephone calls of their traders and other employees to supply evidence in the event of insider trading along with other financial crimes. By utilizing natural language processing techniques, organisations can automatically transcribe these audio files into text. Then AI/ML models can recognise relevant keywords and topics, analyse tone and sentiment, and raise alerts towards the fraud team when suspicious behaviour rises above a given threshold.
4. Eliminating the issue of false positives
False positives would be the bane of fraud investigators' existence, diverting expert resources from the true criminals and alienating innocent customers and employees. You should use AI/ML strategies to build models that can analyse previous cases and separate out the behaviour patterns that are truly suspicious from the purely superficial anomalies.
5. Revisiting rule-based methods
Many current fraud detection systems use a defined set of business rules to evaluate the likelihood that the given case requires investigation. You should use AI/ML models to supplement and test these rule sets. This provides insight into the relationship and relative predictive power of each rule as well as suggests new rules that can be put into increase the accuracy from the results.
6. Identifying collusion
One of the very most powerful tools in an investigator's toolkit is network analysis, which provides tools to visualise and comprehend the relationships between your people, places and events surrounding an instance under investigation. Just like human investigators, AI/ML models can be trained to interpret these complex networks, and can often identify patterns and relationships that traditional approaches might miss.
7. Monitoring networks
The move towards providing digital services for purchasers and remote working capabilities for workers poses new trouble for network security teams, who can no more rely on all sensitive activity happening behind the organization firewall. However, you may also use AI/ML methods to process vast amount of network logs and identify suspicious events in a speed and scale beyond the capabilities of human network administrators.
Putting a platform into action
Ultimately, the threat of fraud within the legal sector has potential for serious reputational and financial fallout, highlighting the requirement for pre-emptive fraud defences. Free coding tends to be the starting point for a lot of organisations within their AI journey, and works perfectly well for small-scale initiatives. But enterprise-grade deployments are highly complex and require a a lot more robust approach, plus scaling with free can be challenging. Among the factors to consider is the need for governance to make sure information is used for its intended purposes, as well as ongoing model testing and monitoring to make sure accuracy and avoid bias. Here, taking a centralised approach is a good way to go. By this, we mean putting an analytics platform in place which assists not just traditional statistical methods, but additionally newer AI/ML-enabled techniques.
