{"id":10174,"date":"2023-04-03T09:00:47","date_gmt":"2023-04-03T09:00:47","guid":{"rendered":"https:\/\/fintech.global\/regtech100\/?p=10174"},"modified":"2023-04-05T15:24:27","modified_gmt":"2023-04-05T15:24:27","slug":"how-is-financial-services-leveraging-ai-for-compliance-purposes","status":"publish","type":"post","link":"https:\/\/fintech.global\/regtech100\/how-is-financial-services-leveraging-ai-for-compliance-purposes\/","title":{"rendered":"How is financial services leveraging AI for compliance purposes?"},"content":{"rendered":"

In a time where AI is revolutionising industry after industry, the increasing need and desire to automate compliance is becoming ever more sought after.<\/strong><\/p>\n

The role of AI in financial services is rapidly increasing, with the introduction of ChatGPT causing deep ripples in the financial industry. With compliance being a permanent challenge for companies to maintain, the ability to automate this challenge provides significant upsides for businesses.<\/p>\n

\u201cWe\u2019re seeing AI play a fundamental role in enabling financial services companies to supervise their business communications,\u201d said Stacey English, director of regulatory intelligence at\u00a0Theta Lake<\/a>. She highlighted that with the modern workplace now powered by modern unified collaborations like Zoom and Teams, many regulated organisations are facing a huge challenge in capturing and supervising these complex multimodal capabilities.<\/p>\n

Theta Lake provides compliance and security for modern collaboration platforms and can capture, compliantly archive, and act as an archive connector for existing archives of record for video, voice, and chat collaboration systems. \u201cOur financial services customers are leveraging AI to review vast volumes of communications which would otherwise outstrip the capacity of their compliance teams to capture and manually review them,\u201d explained English.<\/p>\n

The company\u2019s technology also captures and analyses all of the key contextual and rich media such as GIFs, edits and deletes, which can change the meaning of conversations. English stated, \u201cThe AI not only identifies which communications contain regulatory, privacy or security risks but pinpoints exactly where the potential breach occurred saving reviewers having to trawl through entire meetings or chat conversations.\u201d<\/p>\n

English noted that the industry has seen a real shift from legacy approaches to supervision\u00a0 which was designed for the \u2018now ancient email era\u2019. She said that prior approaches to supervision relied on \u2018highly manual and technically simplistic word-searches and lexicons\u2019, while modern approaches to AI-enabled supervision facilitates more sophisticated analysis of data to identify issues by analysing content in context, to understand where and when financial services-relevant conversations are taking place.<\/p>\n

How does English see this space evolving? \u201cAI can only be effective in supervision and risk detection if it has comprehensive data to analyse, so the complete capture of communications without any blind spots is integral to leveraging AI\u201d. Going forward, she explains, the industry can expect even greater demand for assurance that they are capturing all channels of communications, alongside all of the rich contextual information.<\/p>\n

\u201cFurthermore, there will no doubt be increasing requirements for visibility into AI as Boards and regulators seek assurance. At Theta Lake, we\u2019ve taken several steps to ensure that customers have transparency, including audit reports that demonstrate review processes, indicate where the AI has been triggered and allows firms to oversee AI\u2019s performance.<\/p>\n

\u201cUltimately, as a vendor it\u2019s really important to be involved in industry and regulatory dialogue to inform the future state of AI, so we were pleased to provide feedback to the FCA and PRA\u2019s latest consultation on the use of artificial intelligence and machine learning in financial services.\u201d<\/p>\n

Knowing its use<\/strong><\/p>\n

One of the key challenges for many around the use of AI is how best to exploit it for the needs of a company \u2013 the rise of platforms such as ChatGPT are a sharp example of this.\u00a0 In the opinion of\u00a0Aveni<\/a>\u00a0COO Jamie Hunter, the recent surge of these platforms are \u2018raising the stakes\u2019 both for the tech and how businesses chose to use it.<\/p>\n

He commented, \u201cIn financial services the use of AI is not new, especially in larger organisations, but it has been pretty limited to fairly low risk scenarios and not fully embedded across business models until this point. That is changing as large language models and natural language processing evolve and advance, and this will be expedited in financial services as the emphasis on Consumer Duty and proving customer outcomes through a data-first approach grows.\u201d<\/p>\n

Hunter explained how risk management and compliance have \u2018traditionally been seen as an afterthought or an inconvenient necessity\u2019 rather than a value-filled function. With new regulatory demands, technology is enabling the compliance function to sit as the central nervous system of an organisation by using AI to mine data that can drive business decisions in multiple functional areas.<\/p>\n

\u201cThe use of natural language processing with AI is squarely putting the voice of the customer first \u2013 the outputs are real and specific to what the customers are saying. This means that complaints, concerns and requests from customers as well as the advice they are being offered, can be monitored fully and assessed accurately,\u201d he explained.<\/p>\n

Hunter believes that there are many key areas where AI is starting to change compliance \u2013 however, this continues to evolve and will depend not only on the investment made but the priority it is given and the importance of identifying the right data and inputs to ensure the most effective outputs.<\/p>\n

He continued, \u201cIt will guarantee better identification of vulnerable customers and allows businesses to be proactive rather than reactive, identifying trends using data before they become problems. It is already, and will continue to enhance management information and reporting, and allow far greater coverage of customer service calls and interactions.<\/p>\n

\u201cIt is generating efficiencies through automated Quality Assurance and also can help identify where more training and development is required for staff interacting with customers. Technology is also enabling a greater defence in regulatory compliance, capturing every single customer interaction and analysing it to provide vital feedback.\u201d<\/p>\n

Hunter concluded by stating that he believes large language models will be able to outperform humans in every economically-valuable task within a financial services organisation in the next ten years \u2013 and the industry needs to ensure the adoption of this is done properly with \u2018greater collaboration between the engineers and the financial experts to make this as effective as it can possibly be\u2019.<\/p>\n

A key use of AI in financial services for a while has been its use to support decision making in a wide range of risk scenarios. However, James Brodhurst, principal consultant at Resistant AI, believes financial crime compliance is one area where it could be said that adoption has been slower than for other financial crime use cases.<\/p>\n

He stated that the reasons are several, but include some of the broader misconceptions about AI, such as that it always requires a huge historical sample of data to be trained, or that the results may not be easily explained to a regulator and even disagreement on what constitutes a good result.<\/p>\n

\u201cDespite this, AI does already have a role in improving compliance and we expect this to evolve significantly in the near future,\u201d said Brodhurst. \u201cAdopting AI means assessing carefully the goals of such an initiative. Whilst some FinTechs have made the leap from more rules-driven compliance defences to fully AI-powered detection we believe it will be more common for some time yet to see AI augmenting existing platforms, working alongside existing due diligence capabilities and filling the voids that powerful analytics is best placed to cover.\u201d<\/p>\n

Rather than taking over completely, Brodhurst claims that he sees AI being used to allow compliance teams to focus on truly value-added work.<\/p>\n

He concluded, \u201cAt a time when emerging technologies such as generative AI (such as ChatGPT) offer powerful support to financial criminals, the ability to keep track with evolving crime patterns has never been more urgent. AI is able to rise to this challenge with techniques that allow FinTechs to uncover novel patterns as they develop.\u201d<\/p>\n

Machine learning revolution<\/strong><\/p>\n

The rise of machine learning over recent years has seen its presence in company\u2019s technological offerings increase endlessly. As Vladimir Ershov \u2013 head of data sciences and machine learning at\u00a0Clausematch<\/a>\u00a0\u2013 states, its adoption in financial services has been driven mainly by use cases related to fraud and AML.<\/p>\n

He continued, \u201cHowever, the automating of tasks like gap analysis and contradiction detection in the entire corpus of a company\u2019s policies has long been considered an unreachable dream because of the fundamental challenges of natural language processing tasks.<\/p>\n

\u201cBut today, we\u2019re witnessing mind-blowing progress that has changed the game overnight. In 2018, the introduction of BERT and GPT-2 signalled that we finally had technologies capable of performing deep near-human analysis of texts. Subsequent breakthroughs, including ChatGPT and GPT-4, have showcased not only near-human intellect, but also the reasonable cost of execution and a context window large enough to analyse a complete document.\u201d<\/p>\n

Ershov remarked that these breakthroughs, packaged today into infrastructure, represent a \u2018complete revolution\u2019 in document processing. \u201cAs we see with the unveiling of GPT-4 earlier this month, there\u2019s no compliance task that can\u2019t be automated, up to at least 95% in principle. evolution of AI in the financial services compliance space is exponential, and the slope of progress is already very steep,\u201d he expressed.<\/p>\n

Regulatory compliance<\/strong><\/p>\n

\u00a0<\/strong>One of the most critical aspects of the financial services industry is regulatory compliance, with many complex regulations and increased regulatory requirements for financial firms meaning that a lot of resources and expertise are used to ensure compliance with a changing regulatory landscape.<\/p>\n

According to\u00a0MAPFinTech<\/a>, the integration of AI in financial services compliance, can significantly transform traditional processes and help financial firms to simplify their processes and enhance their compliance practices.<\/p>\n

The firm said, \u201cKey AI-powered tools for financial services compliance which are currently in use by Financial Firms, such as the KYC, AML transaction and monitoring systems and market surveillance, enable financial firms to speed up their transaction screening\/review processes to identify suspicious transactions and potential risks in real-time, eliminating the false alerts and mitigating risks while saving time and resources.\u201d<\/p>\n

The application of AI in compliance is also able to boost the efficiency of compliance operations which traditionally relied on manual processes \u2013 many of these tools powered by AI can help compliance officers with real-time alerts and notifications, enabling them to react swiftly to potential compliance violations.<\/p>\n

MAPFinTech added, \u201cNevertheless, given the strength and capacity of the human brain in terms of learning, comprehension, analysis, rationalism, and decision-making, the human factor remains critical in compliance space and (for now) cannot be completely replaced by AI-powered tools.<\/p>\n

\u201cAlso, firms remain accountable for the final decision, which cannot be attributed to AI systems, thus the human intelligence remains critical but can heavily be supported by the adoption and integration of AI-powered technology.\u201d<\/p>\n

MAPFinTech explained that it believes AI is playing a \u2018critical role\u2019 in the field of financial services compliance, and its significance is only expected to climb as regulations become more complex and the volume of data continues to increase.<\/p>\n

\u201cAs the technology evolves, more advanced AI-powered compliance solutions can be expected, which will provide even greater efficiency, accuracy, and insights into compliance-related activities, with the human factor remaining critical and accountable in the final decision-making process,\u201d the firm concluded.<\/p>\n

Compliance efficiency<\/strong><\/p>\n

In a world where the challenges faced by financial services businesses are multiplying rapidly, the need for efficiency in compliance becomes an ever more welcoming desire.<\/p>\n

According to Joe Schifano \u2013 global head of regulatory affairs at\u00a0Eventus<\/a>\u00a0\u2013 financial services has been leveraging AI for compliance purposes in various ways. \u201cOne of the most significant roles that AI is playing in this space is automating and streamlining the compliance process. AI can help financial institutions sift through vast amounts of data to identify potential compliance violations, reducing the risk of human error and increasing efficiency,\u201d he claims.<\/p>\n

In addition, he stated that another key role AI is playing in the financial services compliance space is fraud detection. He stated, \u201cAI algorithms can identify unusual patterns in transactions and alert compliance officers to potential fraudulent activities, which can help prevent financial crimes.\u201d<\/p>\n

He continued, \u201cThe use of AI in financial services compliance has changed significantly in recent years. It has moved beyond traditional rule-based systems to more sophisticated machine learning models that can analyse vast amounts of data in real-time. This has enabled financial institutions to stay ahead of compliance issues and reduce the risk of regulatory violations.\u201d<\/p>\n

\u201cIn the future, we can expect to see even more advanced AI-powered compliance tools that can adapt and learn from new data, as well as natural language processing capabilities that can analyze unstructured data such as emails and social media. As regulations continue to evolve, financial institutions will need to continue to leverage AI to ensure they remain compliant while also reducing the cost and time involved in the compliance process.\u201d<\/p>\n

Managing complexity<\/strong><\/p>\n

The increasing number of regulations in the financial industry mean that any technology that is introduced is focusing on increasing efficiency and reducing complexity. Joseph Ibitola, growth manager at\u00a0Flagright<\/a>, believes that both AI and ML techniques hold great promise in helping financial institutions find efficiencies in this area.<\/p>\n

He explained, \u201cTheir goal is to deploy AI\/ML algorithms to improve automation, speed and accuracy. Regulatory and compliance staff can leverage AI\/ML algorithms to address risks quicker and focus on the most important issues that threaten their firm\u2019s regulatory standing and reputation.\u201d<\/p>\n

Ibitola cited \u2018promising\u2019 use cases in production today such as AI\/ML algorithms that compute probability scoring of alerts, algorithms that detect abnormal trading patterns through anomaly detection and NLP to monitor messaging platforms and media channels for suspicious activity.<\/p>\n

He continued, \u201cCompliance teams need to be mindful of both what AI\/ML algorithms do and how to deploy them as part of a wider set of technologies. Regulators globally require compliance officers to \u201cexplain\u201d how AI\/ML models determine which activities to promote as an alert.<\/p>\n

\u201cEarly use cases are promising and financial institutions will continue exploring use cases and deploying the latest technology to improve efficiency. This is becoming increasingly critical as regulators globally are doing the same\u2014financial institutions will want to keep pace.\u201d<\/p>\n

Consistency and structure<\/strong><\/p>\n

As a sector, financial services has always been a heavily regulated space \u2013 with compliance requirements consistently changing. In order to comply with these regulations, many financial institutions have traditionally relied on manual processes to review vast amounts of documents and data \u2013 something that makes the process time-consuming and costly. However, Jon Leitner \u2013 president of\u00a0Ascent<\/a>\u00a0\u2013 believes the rise of AI has made compliance processes become more efficient, accurate and cost-effective.<\/p>\n

He continued, \u201cOne significant way AI is transforming the financial services compliance space is through its ability to organize vast sets of information and documents. AI-powered tools can ingest large volumes of data, categorize, tag, and index it, making it easier to locate and analyse. This capability can be particularly useful for regulatory compliance, as it allows financial institutions to quickly identify and track specific transactions, customers, or patterns of behaviour.\u201d<\/p>\n

Leitner also suggested that AI is able to bring consistency to the data structure itself, which can therefore remove subjectively from people\u2019s points of view. This standardisation, he claims, can be crucial in ensuring data is consistent across different departments and can be compared easily. This can substantially reduce the potential for errors or omissions in compliance reports.<\/p>\n

\u201cAI can provide financial institutions with greater flexibility in adapting to changing compliance requirements. AI-powered compliance tools can be trained on new regulations and requirements, and they can quickly incorporate updates and changes as they occur. This can be particularly valuable in an environment where regulations are constantly evolving and can reduce the risk of non-compliance penalties,\u201d Leitner continued.<\/p>\n

He concluded that the technology is \u2018playing an increasingly important role\u2019 in the financial services compliance space by helping institutions to better organise vast sets of information and documents. \u201cAs AI technology continues to evolve, we can expect to see even greater improvements in compliance efficiency and accuracy.\u201d<\/p>\n

Web3 rise<\/strong><\/p>\n

Omnia<\/a>\u00a0CEO Cristian Lupascu also remarked on the topic of AI in financial services compliance, \u201cAs the amount and complexity of transactional data increases, compliance officers are turning to AI and privacy enhancing technologies to help them identify irregular patterns, detect fraud, and improve customer due diligence with a specific focus on anti-money laundering efforts.\u201d<\/p>\n

While AI adoption for compliance has been around for years and continues to grow, Lupascu mentioned that its application in the Web3 space is still new and has \u2018enormous potential to facilitate real-time monitoring and analysis of transactions, while also safeguarding data privacy and security.<\/p>\n

He concluded, \u201cBy automating compliance processes, analyzing blockchain data, and enhancing transparency and accountability, AI in Web3 can be a critical driver in improving compliance. However, the adoption of AI in Web3 is not without challenges, such as the need for regulatory clarity, investment in data quality, and the necessary infrastructure to support AI adoption.\u201d<\/p>\n

Copyright \u00a9 2023 FinTech Global<\/p>\n","protected":false},"excerpt":{"rendered":"

In a time where AI is revolutionising industry after industry, the increasing need and desire to automate compliance is becoming ever more sought after. The role of AI in financial services is rapidly increasing, with the introduction of ChatGPT causing deep ripples in the financial industry. 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