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AI and Advanced analytics can help champion next generation compliance in the financial sector industry

Continuing its series of sessions with the financial sector industry across ASEAN, OpenGov Asia hosted its third OpenGovLive! Virtual Breakfast Insight with delegates from Thailand on 20 August 2020.

The event once again saw a 100% attendance and a great level of involvement from the audience on the topic of Powering Next-Generation Compliance with AI and Advanced Analytics.

Understanding the importance of compliance and risk management for the financial industry in these tough times and the urgency of deploying tech in this process was the major focus of the varied insights from delegates and speakers.

Mohit: Technology can only augment existing human resources, not replace them

The session was opened by Mohit Sagar, Group Managing Director and Editor-in-Chief at OpenGov Asia.

Mohit shed light on the harsh reality of the times where everybody is masking data. Further, the danger from threat actors is becoming more and more sophisticated.

These cybercriminals are leveraging advanced technology with a destructive mindset and without any constraints of regulation or compliance.

Financial organisations cannot afford to lag behind in keeping up with the latest technological developments in the field if they want to protect themselves and simultaneously outdo adversaries to survive.

On the issue of compliance, Mohit felt that it had to be seen as a part of the big picture and not seen in isolation. Compliance must be part of a robust framework that necessitates using AI and Analytics to derive desired and clean outcomes.

However, it is important to understand that technology can only augment existing human resources, not replace them.

He concluded by stressing the need for focused leadership and the need to collaborate with partners who champion the field of technology.

Nutapone: Our objective is to improve lives by making better decisions

After Mohit, Nutapone Apiluktoyanunt, Managing Director, SAS Thailand shared his views on the topic with the audience.

Nutapone began by introducing the company and given a brief overview of the work globally. He spoke about how SAS used analytics to effectively share data around the pandemic with the public using free dashboards.

He shared that their objective as an organisation is to improve lives by making better decisions and they do it by providing a wide range of tech-driven solutions to their customers.

Nutapone then went into detail about other the incredible projects done by SAS in the financial sector industry. These initiatives support areas like digital transformation, customer experience, risk management and fraud & security management.

Making it relatable for the audience, he delved deeper into fraud & security management solutions. Elaborating the myriad of ways in which it can help financial organisations including monitoring, conduct assurance, financial intel and compliance, market and trade surveillance.

He concluded by highlighting SAS’s strength as leaders in the field; not only do they only have solutions for payment fraud and anti-money laundering but are champions in data and analytics.

Ahmed: Various challenges in using traditional AML solutions and how to overcome them

After Nutapone’s presentation, Ahmed Drissi, Anti-money laundering lead- APAC for SAS shared more details into the SAS money laundering Solution.

Ahmed began by talking about the challenges in using the traditional AML solutions; key among these is their inability to cope with the high volume of online transactions. Ahmed then explained how their solutions can help overcome these challenges.

He spoke about other stakeholders in the industries, like regulators, who also recognise the benefits of using AI and ML in anti-money laundering initiatives. These stakeholders are now whole-heartedly encouraging the use of these technologies in the field.

He supported this by sharing examples of financial regulatory authorities in the USA, UK, and Singapore have started recommending the use of AI and ML in the context of anti-money laundering.

Ahmed went on to share a visual graphic representation of the three phases of AI and ML adoption cycle as done by large global and regional banks. The three phases are Innovation, Adoption and Maturity.

This phased approach was a key insight into how SAS helped organisations improve operational efficiency and reduce false positives.

He also enumerated various AI and ML use cases in AML that include: entity resolution, customer segmentation, post alert scoring, model detection, tuning and optimisation.

In concluding, he presented a bouquet of SAS offerings – Financial Crimes Analytics Solutions – that help monitor and prevent fraud incidence in the organisations.

Viswanathan: Banks and financial institutions must make compliance simplified and robust

After Ahmed, Viswanathan Namasivayam, advisor for Data Science Enterprise Architecture, Data and AI group at UnionBank Philippines shared his expert opinion on the topic.

Viswanathan started by pointing out the need for banks and financial institutions to make compliance simplified and robust in light of ever-increasing fraud and hacking incidents.

He highlighted the power of advanced tech like AI and ML. Its efficacy lies in its ability to go beyond a single representation of an individual or an entity rendering a better understanding of fraud risk.

He also emphasised the fact that using tech in regulation and security is non-negotiable and validated this stance with a recent case study from Germany.

To further underscore his point, he shared how supervisors and regulators of the industry are also implementing and encouraging the use of new tech.

He concluded by pointing at the significant paradigm shift in organisations’ approach in handling fraud incidences from initiating action after something irregular has been detected to taking actions to prevent the fraud risk.

Viswanathan was confident that this is was a big step for organisations on their journey towards having a robust risk and fraud management system.

After this powerful presentation, it was time for the polling question session to get all the delegates involved in the discussion.

On the first question regarding major challenges faced during AML investigation process, the Thai audience was split between High rates of false positives (41%) and Lack of data/insights around customers, accounts and entities (41%).

A senior delegate from a major bank shared that not only is the data insufficient, it also lacks accuracy, which makes the investigation very challenging. Furthermore, fraud investigation is not an isolated process, it requires analysing data not just during the time of the incident but also from the surrounding blocks of time. Thus, insufficiency and inaccuracy of data become significant challenges.

It was interesting to see that the response to this question in Thailand was along the same lines as it was in the Singapore session. There too, a majority of delegates voted for high rates of false positives (44%) while the rest of the responses saw an equally distributed portion of votes.

In regard to the next question about the areas that would benefit most from the use of AI and ML the audience was divided among all the available options but two of them accounted for over half the group: Alert triage and risk prioritisation (27%) and Automated disposition of alerts(27%).

A senior compliance officer from a public sector bank shared that she voted for Alert triage and risk prioritisation as in this area, AI can help automise a lot of procedures and processes making them speedier and more efficient. This makes overall work progress fast.

The response to this question in the Singapore session featured an almost equal distribution of responses over all the available options as opposed to a clear inclination towards anyone of them.

On the final question of the expected time to complete an investigation with regards to the enhanced due diligence process, a majority of the audience voted between 30-60 minutes (40%).

When asked for a reflection, another on of our delegate shared that using AI/ML can really help speed up the investigation process, but it also partly depends on how quickly can you get the information from your customer. If your case management system allows you to communicate directly with the customer and get the information embedded in the case, that can really speed up the process too.

It was interesting to observe that when the same question was asked to the Singapore delegates, many shared that they spend more than 24 hours to complete an investigation  – pointing to an urgent need for AI/ML-driven solutions.

After the polling session, Ahmed concluded the session with closing remarks. He thanked all the delegates for their time and participation in the event. He said that adopting tech in regulation requires laser light focus – something that SAS champions. He encouraged delegates to engage and collaborate with them if they are working towards it.


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