Five Beneficial Applications of Artificial Intelligence in Compliance

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With the expansion of international trade, various regulations in banking come on the scene. Financial institutions have to comply not only with their internal rules but also with the laws of different countries and whole trading blocs such as the EU, NAFTA, or ASEAN. Digitalization in financial services could provide companies with a broad understanding of the compliance issues that arise within the financial services sector. This understanding addresses techniques used to implement a successful compliance function in financial institutions and provides a comprehensive introduction to all the issues a global Compliance Officer may encounter. It explores the international regulatory environment, managing the risk of financial crime, governance, risk management, ethics, integrity, potential fraud, cybercrimes, fairness, and money laundering. This results in stricter protection measures to guarantee banks’ and customers’ safety in terms of digital wallets, mobile banking apps, online transactions, etc. Therefore, finance companies are balancing between compliance with manifold rules and the provision of smooth and high standard services to their clients. Non-compliance, in turn, results in fines, reputational damage, difficulties at audits, and even the loss of banking licenses. 

In many cases, firms do not adequately take advantage of the tools and technologies that can help them effectively leverage their resources. AI could be useful for financial institutions to avoid falling behind. AI, as a set of techniques designed to imitate human intelligence in computer systems, offers financial firms tools to lighten the burden of compliance. Below are five benefits of AI in banking and other financial services that help companies to successfully face the compliance challenge.

1. Evolution in data processing

Regulations in banking, such as the General Data Protection Regulation (GDPR), the Second Payment Services Directive (PSD2), the requirements of Know Your Customer (KYC), anti-money laundering and counter-terrorist financing (AML/CTF), and other rules are extremely vast and complicated. Their manual processing inevitably leads to human errors. Recently, Big Data processing technologies based on pattern recognition, Machine Learning, and Deep Learning have been intensively applied to deal with large-scale heterogeneous data. Big Data consists of multisource content, for example, images, videos, audio, text, spatiotemporal data, and wireless communication data. Moreover, Big Data processing includes computer vision, natural language processing (NLP), social computing, speech recognition, data analysis on the Internet of Vehicle (IoV), real-time data analysis in the Internet of Things (IoT), and wireless Big Data processing. In that case, we could rely on AI when it comes to copying and pasting data, creating folders, searching for information on websites, and dividing data into groups. All these assist compliance officers, who work with large amounts of unstructured records, to carry them safely through the large quantities of regulations.

2. Сombination of AI solutions and human management

Just as operations carried out by humans often end up with mistakes due to human error, calculations performed by machines sometimes lead to false positives. Semi-automated processes aimed at compliance with the regulations save time and money, as well as improve performance efficiency. A harmonious combination of human intervention and AI allows companies’ experts to concentrate on developing compliance strategies and solving non-typical problems, while AI-based systems simplify the rules, reduce overlapping in documents, and manage repetitive tasks. AI has the ability to analyze, predict, diagnose, and then provide responsible people with highly detailed reports that help to make the right decision.

3. Keeping businesses aware of the most recent changes

Along with the introduction of new regulations, the amendment of old ones takes place. When this happens, banks and financial companies have to review their documents carefully to make sure they comply with current regulations. Thanks to the ability of appropriate AI algorithms to recognize similar patterns in data, volumes of documents can be clustered into groups. This saves financial experts lots of time, as they can closely study only the contracts and clauses in each group that need to be amended. Additionally, such AI algorithms as graph analytics and entity resolution are capable of extracting information out of complex rules, reducing duplications in documents, and making them simpler. These properties of AI can help compliance officers to avoid the incorrect interpretation of rules, to devote the greatest attention to the regulations that require immediate action, to prioritize the necessary changes, and to assess risks.

 4.  Ensuring customers’ safety

Digital transformation in banking not only opens up fresh opportunities for clients and businesses but also brings about fraud, laundering of money, cybercrime, and data misuse. Therefore, the strict AML and KYC standards become a matter of course.

There are some issues across the financial services with financial institutions failing to secure their mobile apps. As mobile banking is becoming the primary user experience and open banking standards are looming, mobile security must become a more integral part of the institutions' overall security strategy.

When a company fails to consider a proper application security strategy for its front line apps, they can be easily reverse-engineered. This sets the stage for potential account takeovers, data leaks, and fraud. As a result, the company may experience significant financial losses and damage to its brand, customer loyalty, and shareholder confidence, as well as face significant government penalties.

KYC is the responsibility of financial institutions to verify their customers’ data before entering into a business relationship with them. This includes not only names and addresses but also connections with various entities and checking of blacklists. AI, combing through mounds of data, performs client profile verification to enhance the due diligence process in an organization. This helps in identifying high-risk customers as well. AI processing can also be used to perform multitasking by replacing strenuous human effort. Thus, AI saves businesses' money and time, which is then available to be invested in other productive organizational tasks. 

Fingerprints, finger vein patterns, iris, and voice recognition are the part of the AI-based Virtual Payment Processing Tools that ensure clients’ safety, thus helping financial organizations comply with AML regulations. Biometric technology provides the strongest method of authentication that protects confidential information from being compromised by unauthorized personnel.

5. Meeting internal compliance standards

Compliance with internal standards may be as essential for financial organizations as meeting national regulations. Internal compliance can be achieved by means of Natural Language Processing. NLP is an AI-based ability of computer programs to analyze natural languages. Implementation of NLP to analyze bank officers’ spoken and written communication with clients helps companies to comply with their internal standards. NLP also forms the basis for chatbots that allow companies to promptly and effectively converse with their prospective clients.

Conclusion

AI technology is a great helper in meeting compliance with various regulations that have emerged during the era of Digital Banking. Appealing aspects of AI include its ability to extract maximum value from data, aid decision-making, and ease the burdens on compliance professionals. One of its most compelling uses entails automating repetitive tasks, which frees up compliance professionals to refocus their efforts in other areas that may add greater value. The evolution of AI allows financial institutions to capture, analyze, and filter myriad data elements. The main benefits offered by AI and robotic process automation (RPA) are reduction of false positives, reduction of costs, and reduction of human error.