How is AI augmenting compliance practices?

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Forming a significant theme in several organisational technology strategies, AI can augment a gamut of business practices, including compliance. Compliance is a must-do activity, not a nice-to-have. It is essential that companies extract maximum value from compliance processes, reducing the possibility of it being considered a cost centre.

Technological innovation can help to lift some of the compliance burden. The level of technology you can realistically implement depends on how advanced the organisation is to start with. One company’s moonshot could be another’s business as usual. Assessing the starting point is just as important as considering the benefits and end goal.

Hidden Insights: How is AI augmenting compliance practices

How is AI augmenting compliance practices.

RegTech, AI and the future of compliance

This is the question that the burgeoning RegTech (regulatory technology) industry is seeking to answer. AI is typically at the forefront. RegTech partly focuses on improving the efficiency and effectiveness of existing processes. As part of that improvement, organizations are using AI, machine learning and robotic process automation (RPA) to smooth the integration and processes between new RegTech solutions, existing legacy compliance solutions and legacy platforms.

Why look to AI for help? Recent regulations, such as GDPR or PSD2, are handed down in the form of large and extremely dense documentation (the UK government’s guidance document for GDPR alone is 201 pages). Identifying the appropriate actions mandated by these lengthy documents requires a great deal of cross-referencing, prior knowledge of historical organisational actions, and knowledge of the relevant organisational systems and processes. What’s more, several regulations attract fines or corrective actions if not applied properly (like the infamous "4% of company turnover" penalty attached to GDPR).

In short, the practical application of regulations currently relies on human interpretation and subsequent deployment of a solution, with heavy penalties for noncompliance. This is where AI can help, reducing the workload involved and improving accuracy. Here are three key examples of how AI can help companies turn compliance into a value-added activity.

1) Reducing the risk of nonconformity

Following the deployment of compliance processes, there is often residual risk. This can be as a result of unforseen gaps in compliance processes, or unexpected occurrences that become apparent when operating at scale.

That’s partly because there are usually a lot of steps and processes to be carried out during the data collation stage of compliance programmes. RPA can help reduce administrative load associated with these processes that include a high degree of repetition – for example, copying data from one system to another. AI can then help process cross-organisational documentation, combining internal and external sources and appropriately matching where necessary.

AI can also help to reduce companies’ risk of noncompliance with, for example, privacy regulations. Furthermore, using AI techniques, organisations can automate transforming and enhancing data. Intelligent automation allows companies to carry out processes with a higher degree of accuracy.

2) Improving process efficiency

Inefficient processes can also hinder compliance. For example, automated systems that detect suspicious transactions for anti-money laundering (AML) processes are sometimes not always as accurate as they could be. A recent report highlighted that 95% of flagged transactions are closed in the first stage of review. Effectively, investigators spend most of their day looking at poor quality cases.

Use of an AI hybrid approach to detection ensures there are fewer, higher quality alerts produced. Furthermore, it is possible to risk-rank cases which are flagged for investigation, speeding up the interaction and relegating lower-risk transactions. Although AI forms an underlying principle across most modern detection systems, maintenance is key to managing effective performance.

AI can also be used to bolster AML and fraud measures more widely. For example, applying AI to techniques such as text mining, anomaly detection and advanced analytics can improve trade finance monitoring. This, in turn, can improve the regularity for document review and consignment checking, improving the validation rates of materials as they cross borders.

3) Keeping up with regulatory changes

Compliance never stands still. Businesses have to contend with a constantly evolving landscape, potentially across several regions. AI can help to optimise the processing of these regulations and the actions they require, helping companies keep up to date. Companies that need to effectively comply with several differing regulations require a wide range of understanding across all parts of the business. The size, complexity and legacy systems of the business can be significant obstacles.

To mitigate this risk, companies can use natural language processing (NLP) to automate aspects of regulatory review, identifying appropriate changes contained in the regulation and then relaying potential impacts to the appropriate departments. For example, AI could help geographically diverse companies determine whether changes in the UK have an impact on their Singapore office.

Humans still needed

It’s important to note at this point that AI and RegTech are not expected to widely replace humans. We are seeing early AI entries in the RegTech space, but they’re primarily helping with lower-hanging fruit and repetitive tasks. AI is primarily enhancing the work humans do, making them more effective in their roles.

AI does not come without some considerations, however. There is a great deal of focus and scrutiny on associated possible bias in AI deployments. Other discussions are exploring the transparency and governance of applications and questions around who owns generated IP. As a result, it’s essential that AI works closely with humans, enhancing activities and balancing an appropriate level of manual oversight.

Automated systems against anti-money laundering (AML) are not always accurate. 95% of flagged transactions are closed in the first stage of review. Click To Tweet

AI is augmenting compliance practices by providing faster document review, deeper fraud prevention measures and greater contextual insight. It is also reducing noise in high-transaction environments and lightening the documentary burden on staff. From the start of the regulatory review to the end of the compliance process, AI holds part of the overall solution to a more efficient and valuable compliance function. To learn how SAS is helping global banks use AI and advanced analytics, please visit our booth at SIBOS 2019. 

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About Author

Colin Bristow

Director, Pre-Sales Support

A wide range of experience gained over 15 years working with the financial services sector, Colin has spent time advising organisations in usage of machine learning, intelligent systems technologies and analytics for improvement in business advantage. Colin has worked globally in the areas of Risk, Fraud, AML and technology (including Hadoop) for a wide range of financial services firms. He has spent over 5 years working at SAS, and is currently responsible for supporting one of the global banking organisations in setting a strategy and direction for usage of advanced analytics.

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