ASEAN

Takeaways from AIBP Focus: Innovating with AI Technologies and Data Analytics

The BFSI sector was among the first to adopt AI technologies and big data analytics, where data is used to forecast activity, behaviour and trends to predict the future. While the BFSI industry has long been data-intensive and technology-dependent, new data-enabled AI technologies have the capacity to drive innovation faster and further than ever before. 

The BFSI sector was among the first to adopt AI technologies and big data analytics, where data is used to forecast activity, behaviour and trends to predict the future. While the BFSI industry has long been data-intensive and technology-dependent, new data-enabled AI technologies have the capacity to drive innovation faster and further than ever before. 

Setting the stage for the discussion, Chin Wah Mak, Senior Director, Data Center & Compute Solutions, Dell Technologies, shared that for BFSIs to innovate, create and deliver new value at speed in the new data era, they will need timely access to high-quality, trusted data which will allow them to achieve the following outcomes: innovating to provide personalised and integrated experiences, creating smart and connected branches, and developing data-driven business models. 

Today’s panel from the region including AXA Philippines, Citibank, Microsoft, Muang Thai Life Assurance, Standard Chartered Bank, and VMware was hosted by AIBP and Dell Technologies to discuss themes of implementing AI/ ML to increase efficiency and enhance customer experience, responsible AI and data governance, and forming partnerships.

Innovating with AI Technologies and Data Analytics - AIBP Panelists

Implementing AI/ ML to Increase Efficiency and Enhance Customer Experience

Modern financial institutions will be optimised for operational efficiency through the automation of diverse areas in BFSI operations. Furthermore, successfully resolving customers’ pain points will be critical for financial institutions to deliver a superior customer experience and better outcomes.  

Vikram Gupta, Executive Director, Head, Technology – Automation, Data Analytics & AI/ML, Standard Chartered Bank, shared that he is using AI / ML to increase efficiency from content to workflow. This includes multiple pillars such as OCR-led solutions, and building a data lake securely in real-time. 

Ram Kishore Jaladanki, Vice President – Global Program Manager, CX, Citibank, strives to place customers at the centrepiece of what they do. He emphasised the importance of deploying useful technologies, as usually only about 12% of these technologies are actually being used by customers. When speaking about their Customer Obsession Initiatives, Ram shared that they look at trends (customers and industry), and identify the pain points from there. Subsequently, they will be able to map out the customer journeys that addresses the gap and increases customer experience. 

Responding to a question from the audience on whether BFSIs in the region are ready to innovate with AI despite the lack of research and insufficient data analysis due to its complexities, Karthikeyan (Karthik) Rajasekharan, Sales Director, Data Analytics and Artificial Intelligence – APAC, Microsoft, shared that most of his clients often embark on the AI / ML / RPA journey with an AI Centre of Excellence. However, he noted that there is no one standard model that fits everyone. The challenge, he added, is beyond a pure technology challenge, there is also a cultural element that is at play and needs to be thought through as well. Based on his experience, he found areas of innovation focused on (1) customer experience, (2) new business models, (3) backend automation, and (4) risk estimation. 

Chris Slater, Chief Technologist for APJ in MultiCloud, VMware, agreed with Karthik and added that the innovator’s dilemma will always be there. Some of the questions that his clients often ask are how can they innovate without disrupting their core businesses too much and what is the right level of innovation. Chris added that adopting a bi-modal or multimodal with both exploratory and predictive styles of working can address the initial problem statement(s). 

Responsible AI and Data Governance

Responsible AI, which was brought up during the discussion, is often used as an umbrella term for different practices adopted to improve our trust in the usage of AI. This can include many things, ranging from reducing model bias, enhancing data privacy, and more.  

Vikram highlighted that responsible AI would be a key focus moving forward and it is expected to grow in importance over the next few years. To achieve sustainable AI, one key thing to consider includes eliminating model bias to provide accurate data and ensure that you can justify when asked by regulators. 

Responsible AI can also include enhancing data governance, which Karthik added that having a centralised data governance body would ease some challenges with the increasing data complexity today. Karthik shared that cataloging and tagging the data with sensitivity and privacy are important, and further suggested forming a panel that considers what it means to share the data and how the governance model should look upon collecting customers’ data. 

Forming Partnerships with FinTechs and Startups 

In contrast to what one might assume about the fierce competition between industry incumbents and upstart startups, there is a level of cooperation and partnership between the two. 

Abby Oliva-Cenzon, Director, Transformation and Technology Strategic Portfolio, Demand Management and Emerging Technologies, AXA Philippines, shared that partnering with fintechs can help to expand the reach of the various types of available technologies. She added that rather than viewing them as competitors, they work closely together with fintechs from the FinTech Group of the Philippines where they help each other to adapt and scale. 

Nadia Suttikulpanich, Head of Fuchsia Innovation Center, Muang Thai Life Assurance (MTL), agreed with Abby. Nadia highlighted that insurance and banking companies, and startups blend well together. While startups are wonderful in terms of creativity and speed, they lack licenses which bigger insurance and banking companies have. Furthermore, insurance and banking companies have the know-how and the know-who. At MTL, they invest and partner with a range of stakeholders, from insurtech, healthtech and biotech companies. 

One of the largest challenges MTL faces as a health insurer is expanding insurance coverage to people with underlying medical conditions. By investing and partnering with a range of stakeholders, MTL gets presented with a holistic view on how they can improve the insurance of the sick. Subsequently, MTL was able to lead the development of Asia’s first dynamic pricing health insurance coverage for diabetic patients and first health insurance coverage through telemedicine.

Takeaways from AIBP Focus: Innovating with AI Technologies and Data Analytics

Many BFSIs face a surge in big financial datasets, where the combination of growing financial and administrative records, and new, rapidly developing electronic footprints are reflected. Chin Wah also shared that by utilising more complete and granular information, there is a potential to strengthen the data analysis for decision making. However, it is essential to maintain data cleanliness and develop the software capabilities so that the organisation can act on the insights from the data to create new value.

Check out more insights on ASEAN’s banking landscape from our discussions on Elevating Customer ExperienceDigital Technologies for Transformation and Open Banking to gain a broader view of banking in the region! 

Do reach out if you wish to join us at future discussions, or if there are topics in digitalisation you would like to hear more about! 

AIBP

22 June 2021

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