How AI is redefining financial risk management

By bilal hejawi | 29 September 2017

The financial industry continues to shift at a fast pace, impacted by disruptive fintech and regtech, as well as new complex global regulations and a diversified challenging business environment. As a result the costs associated with complying with financial compliance and risk mitigation are rising.

Financial institutions (FIs) that once relied on static rule-based AML/CTF and fraud detections, as well as labour-intensive investigations and reporting, have recently started to upgrade their systems to semi-autonomous solutions that rely on sophisticated artificial intelligence (AI) tools. 

Applying AI in risk management solutions offer users a leap in operational efficiencies, with clear advantages in delivering higher accuracy in investigations and detections and reducing false positives, while also speeding up the overall screening processes.  When powered by AI engines, these solutions can detect unknown anomalies that the rules-and-risk based approaches developed by compliance officers cannot.

In most cases, these upgraded systems allow better scalability. The rewards are plenty, ultimately rewarding AIs with better overall risk mitigation and customer service.

Post on-boarding, FIs now apply due diligence systems that monitor transactions and report findings based on preset static rules and scenarios. With the growing volume and complexity  of today’s business transactions, these legacy systems are less inefficient, producing a high volume of false positives, and causing downtime in operations; thus adding to operational costs.

In AI-powered banking environments, the approach to financial due diligence is built on intelligent machine learning. These systems analyses massive transaction datasets to discover mostly hidden patterns and relationships that characterise group transactional behaviours.

Based on these patterns, present and future transactions can be evaluated and assigned different risk scores, or flagged for manual evaluation in real time.

Such classifications of different group transaction behaviours helps FIs detect suspicious and unusual behaviour automatically, faster and in more reliable methods. Done manually by legacy systems, most of patterns and relationships would remain hidden. Computer power far exceeds the capacity of human teams for detecting relationships and patterns in large data volumes.

What results from applying semi-autonomous risk management systems, are much fewer false positive alerts, reducing workloads and rewarding FIs with sustained operational savings.

The applications of AI are many and diverse, and they’re fast evolving. As an emerging trend, AI covers multiple sources of data, offering natural language processing (NLP), data mining, text analysis, and machine learning, semantics, and more. 

In essence, AI can accomplish whatever functions people do. But efficient AI systems cannot survive without intelligent human input and design, based on data availability and quality.

Human interventions when systems are properly set up and operational are only needed at long intervals. These systems are built to learn from pervious actions, and apply fuzzy logic to weigh in on the probability of one event against another.

AI-powered solutions are built on different core values that impact their performance. One is the size of data available for processing; another is data readiness for contextual processing, such as being time-stamped with variable expiry dates related to certain client documents.

While developers update AI systems with many different rules, they also allow them to generate new rules based on new inputs. Unlike static systems, an AI environment doesn’t perceive data as fixed in values, but automatically defines new parameters based on new or altered data inputs, or when new sources feed it fresh data.

These intelligent machines can also remember case precedents, and step structures that led to decisions, applying the same for similar future cases.

Much of how well an AI system operates in compliance and risk management will depend on the structure and relevance of data. It goes without saying that the more availability of data and the more diverse it is, the higher the accuracy of reporting and the higher the value gained by FIs. It’s also essential that the intelligent systems deployed fit the type of data structures available to FIs. There are many different approaches to implementing AI in combating financial crime and compliance risks. Each AI system is built to address the unique requirements of the environment it operates within and these can vary widely.

While many small and medium sized banks are still hesitant at adopting this intelligent technology, many anti financial crime and compliance risk management solution providers are upgrading their solutions anyway, forcing a ubiquitous adoption of AI in global markets.

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