The Imperative for Regulatory Technology in Finance: In the post-2008 landscape, financial regulators and supervisors are tasked with crafting robust regulatory policies to safeguard banking customers and investors. These responsibilities encompass a range of activities, including documenting their work, monitoring evolving regulatory frameworks, taking legal actions against financial defaulters, and tracking the increasing number of financial entities and individuals within their regulatory purview. The need for regulatory technology arises from the limitations of human efforts and the financial constraints companies face. Given its capacity to process vast volumes of financial data, AI technology is well-suited to simplify and enhance regulatory compliance, providing invaluable support to financial firms in understanding their regulatory requirements from end to end.

Use of AI also poses significant ethical and security risks that banks must proactively address. These include Ethical and Security Risks such as the following:

Data Privacy Concerns: AI relies on existing customer data, potentially raising privacy law compliance issues. Banks should obtain explicit consent from customers for the use of their data in AI applications to mitigate this risk.

Effective AI Implementation: Banks must ensure that AI-powered automated services are seamless and technically robust. Poorly functioning AI chatbots and voice systems can lead to customer frustration, impacting the bank’s reputation.

Unconscious Bias: The algorithms underpinning AI may perpetuate existing biases and prejudices, hindering gender and diversity targets and community outreach efforts. The European Banking Federation recommends minimizing the use of data related to sensitive attributes like gender, age, and ethnicity in AI models.

Diverse AI Development: To reduce bias and ensure fair decision-making, the individuals responsible for creating AI-based algorithms should have diverse backgrounds. This diversity helps to minimize the flaws and biases reflected in AI outputs.

Compliance Monitoring: AI models should be continually monitored for compliance with evolving regulatory and legal requirements. For instance, credit decisions should be based on factual income and repayment capacity rather than generalized propensities.

Cybersecurity Risks: AI implementation requires robust security controls to prevent cyberattacks, intellectual property theft, and unauthorized access to employee and customer data. Banks must invest in expertise and advanced technology to combat evolving cyber threats. 

How will AI Automate and Enhance Regulatory Compliance: AI-based automation can significantly reduce the burdens faced by financial service providers. AI can help transform processes that help manage regulatory compliance Banks and new-age FinTech companies.

Here are five key areas where AI is poised to play a pivotal role:

1. Efficient Documentation Management: AI, with capabilities such as natural language processing (NLP) and intelligent automation, can automate the handling of thousands of compliance documents, streamline the management of regulatory changes, and extract pertinent information, enabling financial companies to navigate the ever-evolving regulatory landscape more effectively.
2. Minimizing False Positives: Traditional methods contribute to a high rate of false positives in the banking regulatory process, often exceeding 90%. AI solutions help regulators sift through Suspicious Activity Reports (SARs) by correlating data with client profiles and transaction records, significantly reducing the number of false positives.
3. Reduction of Manual Efforts: AI aids in extracting actionable insights from the burgeoning volume of unstructured financial data, automating tasks such as monitoring rule changes, conducting regulatory research, evaluating the impact of regulatory modifications, and aligning organizational policies with external regulatory obligations.
4. Interpreting Regulatory Compliance: AI simplifies the interpretation of regulations by comparing a company’s privacy documents to GDPR norms, ensuring that regulations handle customer data. AI applications can extract relevant sections from lengthy regulatory documents, offering valuable insights and facilitating more informed actions.
5. AML and Fraud Prevention: AI tools are increasingly employed to detect and prevent financial fraud, including money laundering and terror financing. Anomaly detection powered by AI identifies irregular patterns in financial data. At the same time, machine learning algorithms report transactions exceeding a specified threshold, contributing to a more robust anti-money laundering and fraud prevention strategy.

Financial institutions and regulators grapple with the evolving demands of financial regulations. AI solutions offer a pathway to streamline regulatory compliance by automating processes and complementing the efforts of human regulators. With a wealth of experience in delivering Big Data, Analytics, and AI solutions, Sun Technologies’ banking domain experts can ensure financial service providers are able to leverage their data for optimal returns.

How we maintain the highest levels of data compliance measures 

Data Retention

We can help you store transaction-related data for short-term, long-term or any specific period of time. Data retention helps provide visibility into system activity, facilitates testing and debugging, while allowing the re-running of failed transactions, and supporting long-running transactions. You can also extend to unlimited capacity for long-term data retention by iterating native, cost-effective object storage integrations.

Data masking

Masking the flow of sensitive data downstream in any workflow can be enabled by setting rules using when configuring AI automation. Using our proven frameworks that establish data governance standards for cash management, treasury auditing and approvals to save BFSI operations from employee theft and compromised third-party applications.

Data residency

Easily fulfil regulatory requirements of each country and conform to data residency rules using our platform expertise. You can bank on our platforms with our partners offering EU and other region-specific data centers to help you comply and not hold back the necessary automation. These data centers ensure to build out a local, centralized location for hosting data in the place of your need.

Data Key Management

Use encryption keys of your preferred services like AWS Key Management Service to gain complete access control over every key’s lifecycle. You can create policies specific to a set of keys and use your own keys to encrypt others in the hierarchy. Our experts will ensure your automation does not lose out on any of performance features by adopting Data Key Management.

Data Audit log

Our platform integration services would ensure accurate maintenance of Activity Audit Logs that enables Team administrators to see a record of users’ significant actions within their organization. This log can be streamed to an external destination to enable deeper analysis and long-term retention. For more information, see Audit log streaming.