The Use of Artificial Intelligence in Preventing Money Laundering and its Influence on Financial Crime.
Artificial intelligence (AI) is already having an impact on compliance activities. The strength of AI lies in its ability to learn and decode patterns. It allows for the control of machines by teaching them to know the best action to take in the context of certain events to maximize performance. Why do we need to use AI and predictive analytics to manage financial crime ? Because these tools are simpler, faster and smarter. Overall, because they are intelligence-based, they can identify true risks rather than false alerts, thus enabling quicker risk investigation. AIs influence is in its ability to speed up processes using the precise intelligence it offers to implement strategies. AIs natural language generation (NLG) software translates data into text with precision. The NLG AI tool is currently being used to automatically generate suspicious transaction reports. It analyses numerical and text data and recognizes contextual patterns and the importance and then converts the data sets into a suspicious transaction report. NLG is transparent such that reports generated can be traced back to the system of record.
In comparison to the manual process, its advantages are that data is processed fast and accurately, anomalies and reporting times are reduced. The influence to adopt AI systems is driven by its ability to continually learn and adjust its algorithms to decipher and remove unwanted information based on the feedback given to the machine. In other words, the machines keep learning, relearning and unlearning. This is more advantageous to complex processes of compliance because machines learn and unlearn fast, unlike human beings. Rather than steal people’s jobs, AI is and will help do them better. Additionally, AI through automated systems can be used to detect fraud and reduce false positives, therefore making transaction monitoring more efficient. The best approach is to subdivide customer into groups by analyzing previous and current customer activity and understanding their risk attributes and behaviors and then monitoring them more productively.
Advanced AI analytics techniques such as segmentation, clustering, profiling, modeling techniques, and statistical interpretation will benefit risk and fraud identification. Financial institutions and compliance officers have long complained about the large volumes of false positives generated and that they have become a hindrance to efficiency. Instead of moaning about high costs and time wasted in catching up with false positives, banks should take advantage of the new technology that wasn’t available for its predecessors. Tech companies have data sets that can be filtered using the latest AI and machine-learning frameworks that can filter false positives quickly and instead give the bank real and potential threats quicker. Faster detection enables faster investigation. This is more efficient in that it reduces false positives, time wasted sifting through data, costs of compliance and without increasing risk.
Because of its precision, AI reduces the high costs associated with false positives. The financial burden associated with false positives is most likely the number of analystsa bank hires to screen new and existing clients against sanctions lists for risk assessment. AI, through automated systems,satisfies the financial institution by screening and providingongoingclient and transaction monitoring for potential risks perfectly and satisfies the regulator when it sees the bank’s adoption of regulatory changes.While using data analytics models, the best practicestrategy is that financial institutions should use behavior-basedmonitoring to identify risks rather than the prescriptive based monitoring approach. The behavioralapproach allows them to categorize customers based on how they are doing business, while the prescriptive approach identifies risks and aggregates clients through known red flags or detection scenarios; which in the past have been shown to create false positives. This use of pre-establishedcriteria should be avoided because it doesn’t account for changes in risk profiles. In using the behavioural model, financial intelligence becomes a daily task, where compliance officers go through sanction lists and scan for negative media while asking themselves risk-related questions to identify possible suspicious patterns and be able to monitor effectively and reduce risk exposure by having a complete visual of the client; internally through their transactions and externally through the public domain. The regulatory intelligence provided by automation leaves little room for error, and suspicious transactions are caught on time.
AI can solve the problems that financial institutions with complex lines of business face in their KYC and CDD processes by providing robust diligence. Two of the issues is the inability to identify risk in a large amount of date quickly and secondly, without errors that come from human interpretations of findings during client onboardingand refreshing of existing reports. AI identifies clients connected to risky individuals and jurisdictions, it identifies shifts in customer business profiles and recurring themes, it analyses adverse news media using internet web technologies. It then creates automated actions that provide digital document generation, alerts, data analysis and therefore reducing false positives which leads to accuracy in reporting and in turn making financial institutions efficient in being compliant and in centralizing the KYC process.
Originally published here.