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How to Use Generative AI to Revolutionize Banking Compliance

Contemporary banking is impossible without a compliance function, which ensures that financial institutions operate smoothly in complex regulatory environments with multiple laws, rules, and standards. Its key goal is to protect banks from the risks associated with regulatory sanctions, financial loss, and reputational damage that may result from their involvement in illegal or unethical activities. Thus, compliance ensures that banks maintain the trust of customers and market players, which is a pillar of a healthy financial system. 

There is no need to add that all banks nowadays strive to streamline their compliance processes, which are frequently lengthy, complex, and heavily reliant on manual labor. In the following article, I will give some ideas on how AI can revolutionize this process.

What are the challenges of banking compliance?

The complexity of compliance processes is a major challenge in streamlining them. Financial institutions must adhere to various state, federal, regional, and industry-specific rules and regulations. Compliance regulations vary depending on the business operations, service offerings, and jurisdictions in which banks operate. This is why financial institutions use various methods to ensure compliance.  

No wonder that compliance function complexity often makes it a protracted process to complete. Even opening an account for a business customer may normally last up to 7 days due to multiple security and risk management checks. They usually include collecting and analyzing data about a customer's identity, risk profile, and financial activities in order to tackle risks linked to money laundering and terrorism financing. Bank employees have to analyze large sets of documents manually, including corporate documents (Certificate of Incorporation, Extract from shareholders register), business documents (invoices, contracts and statements), identity verification (e.g., passports, national ID cards, driver's licences) and proof of address (e.g., utility bills, bank statements, lease agreements) documents.

Of course there are compliance software providers, which help automate this process. The popular ones include Pega, Alloy, Sumsub and many others. They usually provide case management systems, register checks, sanction list search tools etc. All of this helps significantly to increase efficiency, but unfortunately there are no solutions which can fully remove a human from the process. What is the reason?

The reason is simple: traditional programs are still unable to conduct a thorough analysis of customers' proprietary documents. These documents are frequently of different formats and can be easily misinterpreted without understanding the context. Despite all of the automation, we still require people to spend dozens of hours reading contracts or corporate documents in order to complete a corporate structure or verify the nature of the business. And this is a resource- and time-intensive process that always adds to the complexity of the banking compliance process.

So how can AI help?

In fact, AI can take on the hardest part in the banking compliance process, that is the analysis of unstructured documents. Unlike structured documents, which organise data into rows and columns (like databases or spreadsheets) that are easy to analyze, unstructured documents do not follow any specific formats. Such documents may include emails, legal contracts, financial statements, customer correspondence, news articles, as well as other free-form text documents.

AI, particularly large language models (LLMs), can understand the context, meaning and nuances of a text, just as humans do. It can distinguish entities like names, organizations, dates etc., and define relationships between them, as well as classify information on predefined or learned criteria. What is more important, modern AI models can improve automatically over time their understanding and accuracy capabilities by processing the increasing number of documents.

Thanks to deep learning techniques, LLMs have learned to generalize from the data they are trained on. This means they learn common features and patterns in the texts they process and apply this knowledge to the new data in the future. After training on a large and diverse dataset, LLMs become capable of handling previously unseen text formats or document types without requiring targeted retraining. This capability significantly reduces the time and resources they need for completing compliance tasks, while also enhancing the analysis quality and reducing the risk of human error.

Another critical aspect of LLMs is that they continue to evolve as you read this text. They automatically enhance their ability to extract information from documents, understand context, and analyze data in complex scenarios, which means that they will be able to handle documents of ever increasing complexity over time. This evolution will lead to easier risk assessment and decision-making processes.

So far, LLMs can already handle key unstructured documents in banking compliance processes with ease, which can increase automation levels and improve the quality of analysis. The examples of such documents include: 

  • Ownership structure documents: AI can identify and understand the ownership and control structures of corporate clients.

  • Invoices and contracts: LLMs can extract terms, obligations, and conditions that may have compliance implications.

  • Proof of addresses and Banking Statements: AI can verify customer information and financial activities.

  • Financial statements: AI excel in analyzing financial health, sources of funds, and detecting any discrepancies that might indicate financial crimes.

New Horizons for AI application

However, AI applications in the banking industry are not reduced to document analysis. As the example of the Swedish-based fintech Klarna shows, AI can be leveraged for work with customers. Klarna has developed a proprietary AI Assistant for customer service operations, which has replaced 700 human staff. Their assistant can handle an immense volume of customer communications simultaneously 24/7, offering higher quality services. The AI-powered technology ensures rapid response times and unconditional quality consistency. This is just one example of how AI is already transforming the banking industry.



 

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Denis Skokov

Denis Skokov

CPO

Intrepid Fox

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26 Mar

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London

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This post is from a series of posts in the group:

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