Fintechs rely on transaction monitoring software to secure their innovative platforms against increasingly sophisticated financial crimes. They have an obligation both to their customers and to regulators to ensure their financial business is not exploited by criminals. But in the fast-moving and agile world of fintech, they can’t afford to adopt software solutions that slow down the rate of transactions.
With the constant advances in technology and an active and creative enemy, fintech must adopt cutting-edge systems that enable them to reliably and quickly detect, intercept and report any suspicious activities.
The best transaction monitoring software will cope with a rapidly evolving and increasingly complex environment and keep legitimate payments of ordinary customers flowing when adopting the most advanced payment technologies.
Here are 4 key criteria to keep in mind when choosing your AML transaction monitoring software.
According to Deloitte, “financial service providers are investing increasingly in intelligent solutions that use modern technologies to reduce the costs of regulatory compliance.” The rapid uptake of AI-based transaction monitoring software by Tier 1 banks is a clear sign of this trend. Regulators are also lauding the benefits of AI AML, with FATF (The Financial Action Task Force) strongly recommending the use of AI and machine learning for AML and CFT detection. This means that a path toward better AML has already been laid.
Adopting AI AML tools is not a legal requirement, but regulators are becoming stricter yearly with compliance requirements. It seems inevitable that fintechs will be under pressure to adopt the highest levels of monitoring in the not-too-distant future. Furthermore, using AI transaction monitoring tools can help prevent AML compliance fines.
Rules-based vs. AI in transaction Monitoring
In recent years, FATF has called for a risk-based approach to preventing money laundering. This means that financial institutions and businesses must “identify, assess, and understand the money laundering and terrorist financing risk to which they are exposed, and take the appropriate mitigation measures in accordance with the level of risk.” In practice, this means that not all customers or jurisdictions are to be treated equally from a risk point of view.
Customers or countries known to be at a higher risk will therefore benefit from more stringent levels of monitoring. The problem lies in defining who is at risk and what stringencies to apply.
Rules-based, traditional transaction monitoring software means that rules are used to determine which transactions require further investigation. In rule-based systems, the rules must be predetermined and programmed upfront, and it is easy to be either too comprehensive (resulting in too many false positives) or not comprehensive enough (resulting in suspicious activity going undetected).
A new breed of transaction monitoring systems using AI and machine learning, on the other hand, can analyze a staggering array of factors within datasets spotting patterns and anomalies without relying on human input. These systems can derive conclusions from the data alone, thereby identifying unknown patterns and connections. Because AI transaction monitoring software learns from the data itself, it operates more effectively in an evolving ecosystem than rules-based systems; its super power is spotting potential weaknesses before they are detectable by humans and before they become liabilities.
Fintechs are rising in popularity because they satisfy a growing demand for consumer-centric, real-time payment experiences where financial transactions are processed quickly and efficiently. When fintechs get bogged down in the transaction monitoring processes, they become slower, hampering the customer experience. To ensure that speed and agility are not sacrificed in the name of AML, fintechs must invest in the right tools.
Traditional rules-based transaction monitoring software can’t keep pace with business demands because these solutions trigger too many false positives. Manually monitoring so many false positives creates a ton of overhead and slows down operations. At the same time, when false positives are ubiquitous, there is a greater risk of analysts missing the genuine positives. Intuitive AI, however, is a game-changer. With no rules and non-human bias, these tools result in a 90% (or greater) reduction in false positives. They are also fast and accurate because machines are better at spotting even tiny anomalies.
Businesses that go the distance can adapt rapidly to changing realities. The agile nature of most fintech businesses requires transaction monitoring solutions that can adapt to new realities independently without the need for long, cumbersome design and programming. A solution that takes months or even years to program is pointless in a fast-changing reality.
Fintechs need transaction monitoring software that is easy to onboard, and that can grow alongside the business. The ideal software will be agile enough to allow expansion into new corridors as needed or handle increases in the number or amount of payments without collapsing or requiring a huge investment in manpower to manage it. A reliable unsupervised transaction monitoring software solution will support business expansion while keeping compliance costs and systems management manageable.
The Next Steps
AI transaction monitoring is rapidly emerging as the future of the fintech business. As these systems do not need to be programmed (and re-programmed) with rules or have their models updated, intuitive AI systems are faster, easier, and cheaper to onboard. They are ideal for the fast-paced, dynamic financial business arena that exists today. Take note of the points raised in this article and follow the example of Tier 1 banks and regulators who are already working with some of the best solutions, and you are sure to make the best decision for your business.
Learn about transaction monitoring software for fintechs.