How to Use AI and Automation to Transform Cybersecurity Regulatory Compliance in Financial Services
Banking | 6 min READ
    
The COVID-19 pandemic has heightened the need for enhancing cybersecurity regulatory compliance in financial services as there have been rising instances of cyberattacks on financial firms during the past 18 months. While financial firms have always been at the forefront of deploying new-age technology tools in keeping their systems fool-proof, artificial intelligence has shown tremendous potential to enhance the cybersecurity web. By using AI in cybersecurity focused projects, organization can protect their critical assets and, at the same time, guarantee complete compliance with the cybersecurity regulations laid by government entities.
Sanjay H. Chaswall
Sanjay H. Chaswall

Banking & Capital Markets Growth Leader

Life Sciences

Birlasoft

Mark Weston
Mark Weston

Investor & Founder

Regulativ.ai

 
Why Transform Cybersecurity Regulatory Compliance in FS?
Cyber security firm VMware revealed that cyberattacks in the financial sector across the globe grew by 238% from February 2020 to the end of April, with around 80% of financial institutions reporting an increase in cyberattacks. Ransomware attacks shot up 9x during the same period. Capital One bank in 2019 faced one of the largest-ever incidents of a data breach, resulting in an $80m fine and several lawsuits by affected customers.
In 2020, the Securities Exchange Commission in the US warned about a surge in the number and level of sophistication of ransomware attacks on US financial institutions. These cybersecurity attacks only seem to be intensifying every passing day, and with COVID-19, the occurrence has increased multifold.
Financial regulators have laid a significant thrust on cyber resilience and business continuity to bolster resilience. A recent Fortunly report threw some startling stats where it revealed that the cost of mitigating cyber attacks on banks reached $18.3 million annually per company. Manual approaches to counter such threats could make companies more susceptible to such attacks. AI and automation quickly prove their worth in managing the regulatory risk value chain to streamline and speed up cybersecurity compliance.
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How AI and Automation Can Help?
In the light of the high-risk nature of the sector, financial institutions adhere to burdensome global compliance obligations. Additionally, different regions enforce various other compliance norms that keep frequently changing, ultimately making the whole cybersecurity regulatory compliance process a cumbersome affair for fintech. In such scenarios, the deployment of AI and advanced analytics to vast amounts of internal and external data assists in generating predictive, usable insights, helping fintech firms make better cyber decisions and protect their organizations from non-compliance.
Artificial intelligence facilitates compliance officers and institutions to automate all elements of their communications data management, such as capturing data, enriching it with third-party data such as CRM, and the ability to investigate seamlessly, archive, and retain data.
Additionally, artificial intelligence can bring together all data sources, doing advanced searches, and performing complete investigations more efficiently. Artificial intelligence is also revolutionizing financial compliance and redefining how communications compliance risk is managed. Financial institutions can mitigate risk, improve operational efficiency, and reduce compliance costs by proactively detecting and sending alerts on abnormalities in communication, including violations such as insider trading, providing in-depth analysis and breakdown of various communication triggers.
AI and NLP For Cybersecurity Assessment Data Intake
With the integration of AI and Natural Language Processing (NLP), CISOs can enhance their cybersecurity compliance measures by making better sense of data coming out of a security tech stack, which helps them understand where and how various tools and solutions achieve cybersecurity regulatory compliance programs across standards.
With its self-learning capability, NLP keeps improving itself to become more efficient in enhancing cybersecurity processes. Through automation of assessments, business leaders can get real-time risk monitoring insights. Additionally, utilizing behavioral analytics to create baseline markers of normal user behavior, NLP creates a profile for each user and then monitors any abnormal occurrences while continually learning & inferring from new behavioral patterns. Additionally, NLP can analyze and classify documents, obtaining crucial information such as client information, products, and processes that can impact regulatory change. Automating the regulatory change management process is a critical use case of AI.
In 2020, the SEC alone declared 715 enforcement activities, ordering those in violation to pay more than $4.68 billion combined, with the average fine clocking nearly $2M. AI's ability to identify patterns in a vast amount of text allows it to develop insights into the ever-changing regulatory environment and anticipate fines and associated costs. Commonwealth Bank of Australia leveraged an NLP-based AI solution to convert 1.5 million paragraphs of regulatory content into actionable compliance obligations. The pilot project results were highly encouraging as the solution could accomplish the desired goal with up to 95% accuracy. The project also significantly reduced the time required through AI intervention – six months of manual work got completed within two weeks.
How to Use AI and Automation to Transform Cybersecurity Regulatory Compliance in Financial Services Industry
How to Use AI and Automation to Transform Cybersecurity Regulatory Compliance in Financial Services Industry
Machine Learning Models For Assessment
AI/ML solutions can help financial institutions automatically identify, analyze, interpret and even implement to an extent the new/revised regulatory mandates. Fintechs can leverage natural language processing (NLP) and cognitive computing capabilities to proactively, and on an ongoing basis, scan through, evaluate and interpret vast volumes of unstructured regulatory content dispersed across various websites and databases of regulators.
The solution is also capable of automatically shortlisting applicable regulatory requirements for the FI. An AI-powered model can extract metadata and map the new/changed requirements to the financial institutions' products, services, contracts, processes, and functions. The system then translates these requirements into common machine-executable form and links these to the related policies, procedures, and systems of the affected business/compliance functions of fintech.
In a recent joint project between MIT and the University of California at San Diego, researchers have trained a machine-learning model to automatically identify malicious actors through the patterns of their past traffic. Using data from network operator mailing lists and historical BGP data, taken every five minutes from the global routing tables during a five-year period, the machine-learning model was able to identify malicious actors.
Data Remediation for Missing Risk Data Using AI
Financial institutions encounter high volumes of false positives that their conventional rule-based compliance alert systems might be producing. According to a Forbes report, false-positive rates exceeding 90% can forewarn companies about their failed legacy cybersecurity regulatory compliance processes. A compliance officer needs to review these false alarms, resulting in inefficiency and higher chances of human error.
The deployment of AI and machine learning can help streamline compliance alert systems to near-perfection by capturing, extracting, and analyzing critical data elements. This way, AI technology dramatically improves the efficiency of cybersecurity regulatory compliance operations. It reduces costs in today's data-driven regulatory compliance environment by autonomously categorizing compliance-related activities and alerting them to critical updates, events, and activities.
Workflow Automation for Better Collaboration
Workflow automation for cybersecurity regulatory compliance streamlines the flow of crucial information and critical compliance responsibilities. Traditional compliance workflows require a lot of manual effort and input from the compliance officer, resulting in compliance oversight and coordination. Automation simplifies the entire process to adapt to new cybersecurity regulatory compliance norms, which keep changing now and then to enhance security.
Cybersecurity regulatory compliance workflow automation assigns and tracks tasks seamlessly, and then it regularly sends reminders to the people who need to complete the tasks. It also offers visibility into the entire process, such as what has been submitted, what remains outstanding, and whether the job is overdue. In short, workflow automation dramatically improves cybersecurity regulatory compliance oversight, coordination, and collaboration. Automated workflows track real-time communication between CISO's office, regulators, and tech teams and effectively collaborate between multiple teams.
MIT's Computer Science and Artificial Intelligence Laboratory study highlighted that the foreseeable future of cybersecurity would likely revolve around a hybrid approach, with humans and machines working together to running cyber risk management more effectively and efficiently. Automating processes assist CISOs in relieving them from manual operations, enhancing the regulatory compliance landscape, and prompt them to verify at regular intervals about completing crucial assessments, such as the annual Cybersecurity Assessment Tool (CAT) and the Ransomware Self-Assessment Tool (R-SAT).
The scheduled alerts help alert CISOs to conduct annual incident response tests, a gap analysis, and cybersecurity training for employees and the board. Artificial intelligence in cybersecurity is proving to be a gamechanger for CISOs in enhancing cybersecurity compliance programs as a hassle-free affair.
 
 
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