How NLP Helps Automate Compliance Monitoring in Banking
Every financial institution is obligated to follow federal law and faces steep fines for ignoring it. Even fintech startups, banking as a service, and mobile investment apps can’t hide from regulatory scrutiny anymore. Compliance monitoring is more important than ever.
Compliance monitoring, like many modern-age processes, is long overdue for digitalization. Conventional techniques may be standard, but they’re tedious and expensive. Now that artificial intelligence has become more widely accepted, some daring companies are looking at natural language processing (NLP) technology as the solution.
What Is Compliance Monitoring in Banking?
Compliance monitoring is an ongoing evaluation ensuring organizations follow internal policies and external regulatory requirements. It typically involves surveillance mechanisms, periodic reviews, and access permissions to analyze risk and track whether employees and customers comply.
In banking, compliance monitoring is a complex legal process involving consultations with subject-matter experts, independent testing, legal advice, and impact assessments. The goal is to align regulations with internal controls to ensure banks adhere to corporate protocol, industry standards, and laws.
Institutions must follow federal banking laws, actively minimize fraud, and protect consumers from unfair or deceptive practices. Frankly, this work gets expensive. In the United States, financial crime compliance cost $31.7 million annually per firm in 2022, a 14% year-over-year increase.
Still, compliance is necessary. It helps companies mitigate risks before they develop into larger issues, safeguarding them from fines, lengthy recovery efforts, and a poor reputation. However, many institutions are tired of rising costs. Many are turning to AI’s automation capabilities as a solution.
NLP’s Role in Banking Compliance Monitoring
AI is already pervasive in financial services despite various regulatory hurdles. Estimates place its banking market value at $64 billion by 2030, up from $3.88 billion in 2020 — a 1,549% increase in only a decade. Naturally, its high penetration rate has given way to exploration into machine learning subsets like deep learning and NLP.
Since NLP can process unstructured audio, video, image, and text data, it is ideal for dynamic use cases like automating banking compliance monitoring. Depending on what the organization trains it to do, it can easily extract information on relationships, trends, sentiments, or topics.
Once these machine learning models train on preprocessed data, they can extract information from standards and legal documents to generate insights. Financial services can use them to reduce legal advisory hours by 40%, lower compliance content provider spending by up to 70%, and accelerate regulatory-change impact assessments by 75%.
Ways NLP Can Automate Compliance Monitoring
Interestingly, NLP models can automate compliance monitoring in several ways.
1. Translate Legal Jargon
Even within the banking industry, professionals often struggle with legal jargon — they’re experts on finance, not law, after all. When they need help deciphering or summarizing complex language, they can use NLP. These models can automatically “translate” regulatory documents.
2. Compile and Create Records
The chain of custody is a sequential document trail that tracks access, transfer, and control. Although it’s an essential part of regulatory reporting, it is tedious and prone to human error. Fortunately, NLP can automatically generate, compile, and record these reports.
3. Double-Check Decisions
An NLP-powered chatbot or virtual assistant can summarize regulations and answer questions. Financial professionals can consult this automated system to quickly reference a specific law or standard to double-check their choice of action.
4. Produce Impact Assessments
These algorithms can extract information from regulatory agency insiders or press releases to flag upcoming adjustments to banking laws or standards. Decision-makers who anticipate these changes gain more time to update protocols to minimize their noncompliance risk.
5. Classify Legal Documents
Financial professionals can use this model to automatically categorize incoming regulations and standards based on a predefined priority scale or classification system. This streamlines pulling data and referencing documents.
Tips for Making NLP Models More Accurate
The AI adoption rate reached around 72% in 2024, up from 55% in 2023. Such a high penetration rate indicates many organizations have implemented this technology without considering the finer details — like how to preserve accuracy.
While AI can be highly precise in controlled environments, its accuracy may degrade as it processes new information or ages. This is especially true for machine learning models that continually scrape the internet. Professionals can prevent this deterioration by eliminating duplicate values, reducing noise, and transforming datasets.
Another way to increase model precision is to require references alongside output. This seemingly minor addition can substantially reduce miscommunications by making hallucinations immediately apparent. Traceability is crucial in finance.
There’s a Place for AI in Modern Compliance Monitoring
Although many companies in the financial sector are likely hesitant to commit to AI for a process as significant as compliance monitoring, there’s never been a better time to explore such options. Besides, they can always incorporate a human-in-the-loop system to maximize accuracy and avoid minor misclassification errors.
Originally posted on OpenDataScience.com
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