AI Compliance Banks

Will AI Make Compliance Easier for Banks?

IN-COMArtificial Intelligence (AI), Banks, Compliance

Technology has changed the course of every industry. It streamlines processes in manufacturing. It analyzes aggregate data for professional services firms. It enables automation in the agricultural sector. All-in-all, it helps individuals, companies and industries to reach previously unattainable heights.

Some forms of technology, however, are more challenging to implement in regulated industries. The medical, financial, and legal sectors have such stringent regulatory frameworks that it can be difficult to harness the power of many new technologies while ensuring total compliance with regulatory guidelines. But regulations alone won’t prevent paradigm-shifting innovation, like Artificial Intelligence, from making inroads.

In the banking industry and adjacent sectors, AI is destined to become a compliance tool. It will not only help financial institutions to meet decades-old regulations, but it will  help them to remain compliant as they adopt newer technologies. This is a significant step forward for the sector. It means that these institutions can increase efficiency in their systems, optimize their processes, and streamline their operations, creating higher profits and reduced expenses. It will also allow organizations to better serve consumers and meet customer demands and expectations. 

Regulatory Change Management

The regulatory environment for banks is a dynamic one. Compliance guidelines continuously shift, making it challenging for banks to stay on top of each new change. When regulations change, it is the responsibility of each institution to thoroughly analyze which of those obligations will impact the institution and how big the impact will be. Once they determine the new responsibilities, they must design a strategy for implementing them. Finally, after the new compliance process has been implemented, they must monitor it to ensure that it stands up to regulators’ checks and tests. 

Every step of this process is typically done manually. Not only is this incredibly time-consuming, it’s also uniquely complicated. But through a combination of AI and RPA (Robotic Process Automation) much of the process will soon be automated. Financial institutions will be able to put natural language processing to work on any new regulation documents. The AI technology will  ‘read’ the document and identify the new obligations, using these to determine exactly how the institution will be affected. 

Once the essential new insights have been identified, RPA can step in. The insights can be fed into the RPA technology, which will then design the most optimized workflows for integrating and monitoring the changes.

Eventually, the change management process for all new and revised regulations will be completely automated, allowing financial industry IT workers to focus on filling any gaps and ensuring total compliance. 

Anti-Money Laundering

For decades, banks and other financial institutions have struggled to combat money laundering. The illegal activity can be challenging to spot, requiring thousands of highly trained individuals to parse through financial records with a fine-toothed comb. When financial institutions fail to catch money laundering, or their anti-money laundering infrastructure is found to be insufficient, they can run into steep fines from regulatory bodies and waves of negative media attention. Fortunately for them, AI has the potential to improve and elevate this process.

There is AI software that assists financial institutions by analyzing customers’ wider networks—networks that are transactional in nature, publicly available, and internal. The technology can quickly scan through these vast amounts of data and pinpoint any questionable activity that may point to money laundering. This information is then handed off to an anti-money laundering analyst, who investigates the incident further. In the end, the technology allows financial institutions and their anti-money laundering divisions to concentrate on higher-risk incidents, while the technology does the preliminary task of monitoring bulk data. 

On top of this, there are more technologies entering the industry that further streamline the anti-money laundering infrastructure of financial institutions, allowing them to enhance their compliance. The additional AI further automates the process of investigations. The technology would work side-by-side with smaller teams of investigators to more quickly eliminate cases where no money laundering occurred. 

In the end, financial institutions waste fewer resources in terms of both time and people-power. The technology provides better results, stricter adherence to regulations, and allows for a smaller workforce. 

Know Your Customer (KYC) Data Remediation

Financial institutions are facing stricter and stricter regulations with regards to managing financial crimes risk. One such regulation requires them to maintain customer data quality, and currently, banks must maintain the data manually. As a result, many institutions are looking at using a combination of RPA, NLP, and chatbots to accomplish this. 

Their goal is to automate much of this process. RPA could automate the start by flagging any customer profiles with KYC information that is outdated, mismatched, or missing. It could then launch a workflow to resolve the incorrect data. NLP could also be harnessed to mine customer financial documents, emails, or other text-based interactions with the institution for KYC data. Finally, chatbots could fill in any gaps. Institutions could integrate chatbots on their various platforms to seek any additional information that is needed from customers. These could be customized and personalized, ensuring that customers are only asked for the specific information that is needed for their account, rather than generalized questions that could end up wasting a customer’s time. 

An actual analyst will only need to be brought in once AI has finished gathering this data. At this point, all the analyst has to do is review the KYC data updates and accept or reject them. In the end, this would significantly reduce time spent on KYC and the strain of KYC regulations.

Financial institutions can look forward to the power of AI driving their systems and processes forward. It will empower their compliance teams, strengthen the frameworks and assist them in keeping pace with any regulatory changes.