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Gaw Capital
Advancements in RegTech have been impacting business and process innovation
Gabriel Chan
To combat with the exponential growth of FinTech, the traditional bank has also been more aggressively to adopt innovative products in this new digital generation market. However, as the legacy regulation is still catching up with the emerging development, the risk becomes phenomenal. Some governments and regulators have been closely involved as an advisory body when promoting FinTech development. While some was taking a more proactive role, such as the Hong Kong Monetary Authority (HKMA) has granted a total of 8 virtual banking licenses in early 2019. We would expect there will be more regulation as the domain continuous to growth.
Compliance risk remains high
In addition, since the crisis in 2008 the regulatory environment has continued to shift and stringent, banks were getting billions of dollars fines due to misconduct and non-compliance violation. While more legislation and regulations were introduced such as FATCA, MiFID II, GDPR Which has created not only uncertainty and complexity but also significant costs.
Compliance practitioners continue to identify managing the continuing regulatory change and to cope with the control on the innovative technology as their biggest challenges. In my view, Cybersecurity
and RegTech are further enablers to an expanding range of digital services and be sustainable, it’s equally important to ensure a secure service for the clients and to address the regulatory concern.
Regulatory technology
Traditionally, KYC/onboarding processes tend to be paper-based, it requires face to face meetings or physical verification of documentation. In relation to suitability, there is an increasing need for a lengthy analysis of a client's financial circumstances, investment objectives, and risk tolerances. FinTech disrupted all that paper- based manual operating model, it does not incorporate face to face meetings or customer interaction but purely digital and online. With the emergence of FinTech comes, the regulations may soon be revised or extended, and the control function should be reshaped. Recently many institutes were exploring the use of RegTechsolution as an aid to build a more scalable and cost-effective process, here are some examples.
Transaction Monitoring
Transaction monitoring is a key function of AML is the reporting of suspicious transactions, mainly based on some static criteria such as sanction list, payment threshold, and keywords filtering. And is a labour-intensive operation of AML compliance. RegTech solution makes use of Machine Learning (ML) technology to perform big data analysis on all transactional information and customer behaviour. It can more accurately flag those suspicious transactions with less false positive, also helps effectively categorize and rate the unknown activities to prioritize for further investigation.
Internal Surveillance Monitoring
Financial Institute requires to ensure the business process or individual practice comply with the company policy and regulations, such as market abuse, insider trading, conflicts of interest and other non-compliance violations. The internal control function was usually monitoring by keyword search and static filtering rules to detect any potential violation from different channels including emails and instant messaging. Since the communication data is usually in un-structural format, which would be even more complicated than transaction monitoring. By using AI to perform comprehensive big data correlation, RegTech solution can significantly increase the monitoring effectiveness.
Voice Recording Review
Another challenge in compliance monitoring is the voice communication review, for example, in the old fashion trade confirmation voice call recording, or FinTech was using voice as a user interface for client verification. Natural Language Processing (NLP) technology can help accurately recognize and convert the speech into a text script. The automatically generated searchable context would enable for further control monitoring and investigation analysis. In the old days, the control effectiveness varies as highly depends on the unmanageable human factors listening to the voice records word by word. With RegTech, the computer-generated result should be more consistence and is possible for automated process integration. Additionally, AI could provide value-added insight from the conversation such as recognizing the speech emotion. Just be aware, the conversion accuracy for non-English or local languages could be lower as it will depend on the technical design and its data model sampling size.
Expense or Investment?
Traditional institutes with limited resources may have to perform the monitoring on risk-based approach or sampling basis. However, the manual sampling approach is not efficient nor effective, and is not dynamically scalable to cope with the fast business change.
People worry about the AI machine may take over the human job as in the movie? If I use the experience in cyber security, our team was in fact developed a lot of automation tools to assist ourselves in managing the huge amount of security events. Hence, we can free up the limited manpower to perform the more advanced forensic investigation case.
I believe it shall be similar with RegTech, the pressure on the team is always high as to adhere to global regulatory demands. With the advancements in RegTech, it could filter and extract the real valuable insights, helping to unleash the collective power in your well-trained professional people. The investment eventually creates a more nimble and scalable control process to help the company comply with the increasingly stringent and ever-evolving regulatory changes.
Transaction monitoring is a key function of AML is the reporting of suspicious transactions, mainly based on some static criteria such as sanction list, payment threshold, and keywords filtering. And is a labour-intensive operation of AML compliance. RegTech solution makes use of Machine Learning (ML) technology to perform big data analysis on all transactional information and customer behaviour. It can more accurately flag those suspicious transactions with less false positive, also helps effectively categorize and rate the unknown activities to prioritize for further investigation.
Internal Surveillance Monitoring
Financial Institute requires to ensure the business process or individual practice comply with the company policy and regulations, such as market abuse, insider trading, conflicts of interest and other non-compliance violations. The internal control function was usually monitoring by keyword search and static filtering rules to detect any potential violation from different channels including emails and instant messaging. Since the communication data is usually in un-structural format, which would be even more complicated than transaction monitoring. By using AI to perform comprehensive big data correlation, RegTech solution can significantly increase the monitoring effectiveness.
Voice Recording Review
Another challenge in compliance monitoring is the voice communication review, for example, in the old fashion trade confirmation voice call recording, or FinTech was using voice as a user interface for client verification. Natural Language Processing (NLP) technology can help accurately recognize and convert the speech into a text script. The automatically generated searchable context would enable for further control monitoring and investigation analysis. In the old days, the control effectiveness varies as highly depends on the unmanageable human factors listening to the voice records word by word. With RegTech, the computer-generated result should be more consistence and is possible for automated process integration. Additionally, AI could provide value-added insight from the conversation such as recognizing the speech emotion. Just be aware, the conversion accuracy for non-English or local languages could be lower as it will depend on the technical design and its data model sampling size.
Expense or Investment?
Traditional institutes with limited resources may have to perform the monitoring on risk-based approach or sampling basis. However, the manual sampling approach is not efficient nor effective, and is not dynamically scalable to cope with the fast business change.
People worry about the AI machine may take over the human job as in the movie? If I use the experience in cyber security, our team was in fact developed a lot of automation tools to assist ourselves in managing the huge amount of security events. Hence, we can free up the limited manpower to perform the more advanced forensic investigation case.
I believe it shall be similar with RegTech, the pressure on the team is always high as to adhere to global regulatory demands. With the advancements in RegTech, it could filter and extract the real valuable insights, helping to unleash the collective power in your well-trained professional people. The investment eventually creates a more nimble and scalable control process to help the company comply with the increasingly stringent and ever-evolving regulatory changes.
See More: Top RegTech Companies in APAC
The articles from these contributors are based on their personal expertise and viewpoints, and do not necessarily reflect the opinions of their employers or affiliated organizations.
