AI in Compliance: How Banks Can Streamline the Process


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With the rise in regulatory mandates and strict implementation timelines, AI presents a solution to modernize compliance. It helps automate manual tasks, analyze data, monitor new regulations, and minimize errors- all contributing to robust compliance.

AI can process massive data logs and deliver meaningful insights. So it can offer banks real-time updates for simpler compliance management. Moreover, it helps avert compliance risk, streamline compliance processes, and improve productivity.

Why do Banks Need AI to Streamline Compliance?

As per a recent survey by Simplifai, “The 2023 Banking and Insurance Survey,”

The 2023 Banking and Insurance Survey

Standard compliance processes- manual and rule-based are time-intensive, labor-intensive, and prone to errors. Banks must seek ways to enhance their compliance operations to cope with the changing regulatory landscape.

AI-driven compliance systems will help easily scale with growing data and regulatory needs. It enables banks to adapt to changes quickly and effectively in this dynamic compliance ecosystem. These AI systems can update their algorithms and models to align with the latest compliance standards. Such levels of flexibility help banks avoid the risks associated with non-compliance.

Old compliance processes often struggle to extract valuable insights from unstructured data sources.

AI solutions can process and analyze unstructured data, uncovering hidden patterns and relationships indicating potential compliance risks.

AI-based compliance solutions offer banks valuable insights and suggestions to improve compliance strategies. By analyzing historical data and identifying patterns, AI helps banks optimize risk management processes and assign resources more effectively to mitigate compliance risks.

How AI Can Streamline Compliance?

1. Automate Compliance Process

Banks follow old methods to collect data from different systems and create regulatory reports. However, these methods are time-consuming, not dynamically scalable, or easy to integrate with other services.

AI automates data collection, increasing the speed and quality of decisions. It also improves the readiness to meet regulatory compliance commitments. For instance, risk-scoring automation enables banks to make their systems fault-tolerant and compliant with regulations.

2. Facilitates Faster and Secure Transactions

AI-based banking solutions use ML for extracting and standardizing data to enable a seamless and automated wire transfer. It includes data on payment amounts, accounts, history, and other transaction details.

For instance, AI can suggest specific ATM transaction amounts for quick withdrawal. This helps banks optimize various calculations and reduce network latency for quick transactions.

Furthermore, increased speed increases security risk, so AI helps detect fraudulent transactions.

3. Easy Tracking of Regulatory Change Management

As per a recent report by Thomas Reuters, “2023 Cost of Compliance,”

2023 Cost of Compliance

Banks must track and respond to regulatory changes to prevent penalties or risks. Natural language processing (NLP) can extract relevant information and streamline regulatory change management by assessing and classifying documentation.

NLP-based AI solutions can help track if correct protocols are being followed. These systems also help determine processes impacted by a regulatory change to help banks keep up with it.

4. Enhanced Accuracy and Reduces Costs

AI systems can process data faster and more accurately, minimizing the risk of errors and false positives. This enables banks to identify compliance violations promptly and assign resources quickly.

Automating manual compliance tasks helps reduce operational costs. This is because AI-driven systems can handle vast data volume, eliminating the need for time-consuming manual reviews.

5. Sanctions Screening and Transaction Monitoring

As regulatory demands become complex, the risk detection capabilities offered by legacy systems are slow and error-prone.

Banks can use AI, ML, and cognitive analytics to streamline their screening processes to overcome this. It ensures that the screened data is accurate. Al also refines filtering parameters, improving efficiency.

Banks can use their current rule-based approach and augment transaction monitoring with advanced AI analytic tools. This way, analyzing vast amounts of transaction information becomes easy.

6. Third-Party Investigation

Data-driven insights are critical when addressing anti-bribery and corruption due diligence needs. AI and ML can help banks improve verification methods for third-party data before risk assessments.

Moreover, obtaining a single view of critical business partners and suppliers across systems and touchpoints is challenging. AI streamlines compliance efforts by addressing these challenges. When fed into business intelligence systems, this data enables process visualizations.

What are the Compliance Risks Associated with AI?

The key areas where AI helps banks are credit risk management and fraud detection. At the same time, compliance and policy frameworks also use AI tools.

In addition, regulators have expressed concerns about AI’s use in the business. The concerns are about the bias in algorithms used for credit decisions and chatbots’ sharing of inaccurate information.

Simplifai’s survey also states that-

Simplifai’s survey

With that being said, AI adopters face increased risks like-

  • lawsuits
  • bias
  • Lack of traceability due to the “black box” nature of AI applications
  • Threats to data privacy and cybersecurity

How Banks Can Overcome the Risks Associated with AI?

While AI streamlines compliance processes, it is essential to ensure its correct use.  Banks must also ensure they do not rely solely on tech to manage compliance.

Implement AI applications in non-customer-facing processes or to support customer-facing employees. This improves operational efficiency and augments employee intelligence by offering insights, recommendations, and decision-making support.

Here are a few other recommendations-

1. Keep Compliance Experts in the Loop

Keeping compliance experts in the loop is crucial when developing and deploying AI. An internal compliance team will help deal with the risks associated with AI. It will foster collaboration between AI systems and banks to meet the changing regulatory needs.

Having a robust compliance program with experienced professionals is vital. This will help understand regulatory requirements and make informed decisions. Ensure that the compliance experts continuously monitor and participate in the decision-making process. This helps address issues as they occur.

2. Define Objectives and Monitor Performance

Define the goals for the AI system. Implementing KPIs can help understand whether the system accomplishes its role. It also helps measure its performance and revise the outcomes if required.

3. Conduct Regular Audits and Have a Contingency Plan

Conduct regular audits of the AI system’s operations, data sources, and model performance. It helps maintain accountability and resolve potential issues as they emerge.

At the same time, AI tools can malfunction or have errors. Having a contingency plan will help prevent harm and minimize potential disruption.

4. Detection of Biases and Mitigate

Even the best bias-detection algorithms need human oversight. Check AI’s fairness regularly to ensure no disparities in AI’s decision-making.

Two Success Stories

Many banks have already adopted AI-powered compliance solutions, significantly improving their compliance processes. Here are two success stories.


HSBC invested in AI-based solutions that reduced the validation time for KYC by 80%. These systems analyzed customer data, identified potential risks, and alerted compliance officers for further investigation.

The bank uses Google Cloud’s Anti Money Laundering (AML) AI to detect suspicious activities and potential money laundering cases. The system flags unusual transactions. It has enabled the bank to examine and report possible violations promptly. Due to this, HSBC experienced a significant reduction in false positives, saving valuable time and resources.

2. Danske Bank

Danske Bank implemented a modern enterprise analytic solution using AI. Each AI challenger processes data in real time. The bank was able to realize a 60% reduction in false positives.

Danske Bank identified patterns and anomalies in customer behavior. This helped them protect their reputation and safeguard their customers from financial losses. Moreover, compliance officers reported increased productivity after AI’s implementation.

They could direct their expertise on more complex investigations and strategic decision-making. This shift in responsibilities enhanced the overall effectiveness of the compliance team.

Also Read: Why Risk Management Is Crucial to Fintech brands AML Compliance


Data is the cornerstone of a great AI system. Ensure the AI system uses only high-quality, up-to-date data from reliable sources.

Integrating AI into regulatory compliance offers robust efficiency and accuracy when done right. These solutions must align with the compliance requirements. Hence, when designing a new solution, ensure it addresses all these factors.

Using AI in compliance for banking enhances operational efficiency, reduces risks, and strengthens the bank’s overall regulatory position.

Apoorva Kasam
Apoorva Kasam
Apoorva Kasam is a Global News Correspondent with TalkCMO. She has done her master's in Bioinformatics and has 18 months of experience in clinical and preclinical data management. She is a content-writing enthusiast, and this is her first stint writing articles on business technology. She specializes in marketing technology, data-driven marketing. Her ideal and digestible writing style displays the current trends, efficiencies, challenges, and relevant mitigation strategies businesses can look forward to. She is looking forward to exploring more technology insights in-depth.


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