How to Use AI to Fight Financial Crime

Artificial intelligence (A.I.) is heavily used in Big Data and when it comes to the analysis of customers’ behaviour. There’s also anti-money laundering (AML) AI; it’s used to fight financial crime and guard the reputation of app providers. FinTech is about trust, after all. How exactly is it done and how can you benefit?

Because cybercrime is serious, there is a special word for it in the financial world. To be exact – for criminals themselves. They are called “bad actors”. The term is reserved for individuals or companies abusing applications’ privileges. To deal with threats, FinTech companies grow huge amounts of data and draw connections between them.

It’s especially true when the product is complicated and has many features. More than a few things can go wrong, creating legal and marketing challenges. The AML AI is used to fight money laundering and it’s using highly sophisticated algorithms to do that.

How to fight money laundering?

These algorithms are made to analyse big amounts of data and inform the application’s crew if something suspicious is on the radar. Automated processes like customer due diligence (CDD), transaction monitoring inputs, or sanctions screening help spot potentially dangerous activities.

Machine learning can be helpful as well. Even more, since AML AI can make an “educated guess” about whether a particular behaviour matches a definition of a money laundering incident. If it detects an occurrence that varies from the norm, it can send a report (suspicious activity report – SAR) to the authorities. Officials can be notified of non-standard transactions and flag them as potential threats to public safety. SAR is required when transactions reach a certain value level or there is unregular account activity.

It’s always important to implement a know-your-customer (KYC) solution. On one hand, applications need access to customer data. To work properly and offer needed functionalities. On the other hand, they need to have some sort of safety mechanism to not abuse people’s trust in the product itself. The AML AI software needs to react to situations, not invade privacy. 

Fighting financial crime with processes

Nothing happens by accident. Few processes below utilise the power of financial crime technology.

Customer due intelligence is about assessing your customer’s background to establish identity and assess the risk the customer possesses to the app. You can achieve it by gathering personal information, like name, address, some sort of ID, etc.

There’s also enhanced due diligence (EDD). You can do it when you have a doubt about the source of funds or the user’s wealth. Also, when you potentially question the nature of business intentions or the specific transaction on the account. 

Transaction monitoring involves things like sanctions screening, customer profiling, and blacklist screening. It’s about the identification of suspicious behaviour, increasing automation for minimising human oversight. It also helps increase effectiveness with AML technology solutions and boost the confidence of regulators and business partners.

Sanctions screening is important when you want to keep up with changes. The list of sanctions that money launderers, arms dealers, narcotics traffickers, human rights violators, and terrorists face, often changes. Plus, the law itself has changed. In the past, sanctions targeted states or organizations. Today they are focused on the economic sector and individuals. 

If you’re struggling to keep up, the list below for guidance:

• EU consolidated list of sanctions UN consolidated sanctions list

• UK’s HM Treasury sanctions list

• U.S. Department of treasury’s sanctions list

Unstructured data – the threat to AI AML compliance

The problem with Big Data is that it’s… big. The bigger problem is the potentially unstructured nature of the data itself. That’s why it’s so important to have proper asset management software in place. Data-driven decision-making is not the future – it’s today’s necessity. AI AML compliance solutions are good, but the plan to use them is even better.

The law regarding AML AI software and its business consequences 

The obligation to report suspicious transactions is mandated in the Financial Action Task Force’s (FATF) recommendations document. It may not be the law per se but it’s a good starting point from a global money laundering and terrorist financing watchdog.

The actual law comes from these:

• General Data Protection Regulation (GDPR). Established by the European Union, the law protects data and privacy in the EU and European Economic Area (EEA). Companies that store and process the data have to take technical and organizational measures to meet these new standards.

• The 5th anti-money laundering Directive. Another EU-based law. It was established to enhance transparency, limit the anonymity related to virtual currencies and providers of virtual wallets, and pre-paid cards. 

• FinCEN Final Rule (CDD). It's the United States’ law that aims to establish customer due diligence among all appropriate market players.

• Over-the-counter (OTC) derivative rules (Dodd-Frank, MiFID II, EMIR). These types of contracts are always custom and privately negotiated. They include credit default swap and interest rates swap.

• Tax compliance (FATCA, CRS, 871m).

It’s a lot to understand and properly implement in your application. Not all companies can swiftly operate in these waters. Let’s dive in a little deeper:

• Since 2008, European-headquartered banks have been fined a total of $18 billion. That’s by U.S. regulators, for AML and KYC alone.

• Europe accounts for 7% of global AML fines levied in the past 10 years, totalling over $1.7 billion across 83 separate fines.

• In the past 10 years, institutions headquartered in the APAC region have been fined a total of $1.3 billion by US regulators.

The potential cost of doing business in the FinTech industry is very real and you can’t afford to make a mistake and ignore it. An interesting survey comes from an IBM report on the fight against money laundering. Few highlights:

• Investigations take too long (45% responders).

• There are incompatible tools for the security and compliance audit (42%).

• There is a high number of false-positive or unsubstantiated alerts (staggering 40%).

How can you avoid these threats? By paying attention to the software’s architecture. The TSB research shows that four in five people are tricked by fraudulent bank messages. The study by Digital Shadows says that the darknet is full of stolen credentials, allowing it to take over 15 billion accounts. The study from the Association of Compliance Officers in Ireland tells us that remote work increases the risk of becoming a victim of financial crime. What does all of this have to do with your application?

Unfortunately, a lot. The report by pymnts.com shows that 80% of experts point out that utilising AI reduces payments fraud. Almost 64% of financial institutions think AI is crucial to stopping fraud, especially ongoing ones. AML AI can help you in the fight against money laundering but it can only be efficient if the application itself is done well. The back-end is the backbone.

Choose a software partner that can secure your compliance

The most important factor to take care of is IT and software development consulting. Financial software development is not easy. You have to think about fragmented data, refactoring quality assurance, UX, and UI design. All of them can help you overcome a complicated issue with the code and the nature of your application. Machine learning is a powerful tool, but someone has to do it right. Think about knowledge and experience when choosing your partner.