ABeam Addresses Digital Transformation in Financial Services Industry (FSI) - Challenges Lie Ahead | Insights | ABeam Consulting Thailand (2024)

Due to their disruptive nature, breakthrough business models often get a lot of attention. New digital systems and processes enable what was not previously possible. New models emerge by rethinking how to do things with our new digital tools. Examples of the kinds of services made possible include alternative credit scoring and dynamic pricing models for insurance. Both of these services utilize alternative data sources such as those sourced from social media, fitness trackers, satellite data and other online sources to more effectively price risk. As more data is gained on people, customers will no longer be segmented by the same old categories of age, sex and nationality, but rather by behavior. And as more customers are incentivized to bring their devices online and share their data, the vast amount of information collected can provide insurers a new way to benchmark and categorize risky behavior and implement alternative credit scoring effectively.

Imagine a world where insurers and lenders can provide real-time feedback and pricing based on a customer’s activities. Insurance companies could give customers a greater understanding of the costs associated with the choices they make, and provide financial incentives to change their behavior and move into a lower risk category. A driver who is speeding could be informed how much his premium will increase next month if he doesn’t slow down, or an individual exercising for 30 minutes every day could be rewarded with a lower premium if they hit that milestone consistently. Data will generate insights into customer behavior which will affect risk pricing and customer engagement. Insurers can financially incentivize their customers to lower their risk profile, therefore reducing the amount of money they will pay out as claims and improve their bottom line.

Some Lenders are using non-traditional data such as satellite data, weather forecasts and crop price predictions to more accurately forecast the ability for farmers to repay loans. ABeam has partnered with Orbital Insight¹⁴to utilize their cutting-edge analysis of aerial imagery and other non-traditional data. This could be used to accurately predict and measure flood risk and predict crop harvests, resulting in more accurately priced risk profiles. Other lenders in the financial sector have begun using non-traditional data such as information gathered from social media in their risk management and pricing models.

Big data is essential to manage machine learning and AI, and it is supercharged if it incorporates non-traditional data such as those obtained from wearables, vehicles, online services or other non-traditional sources.

Developing strategic solutions and designing and implementing the systems that are required to run them, puts us in a unique position to understand the possibilities that arise. We have leveraged our knowledge in technology and business strategy development to help insurance companies in Thailand to develop new business models and digital strategy to compete in this digital disruptive era. Our clients have benefitted from tools such as Hypercube, which incorporates analytics and Artificial Intelligence to provide new insights into their business processes. Not only has this allowed our client partner to increase lead conversion, improve customer engagement and increase cross selling of other products, these analytics can also improve profitability by better identifying fraudulent claims.

One of the most significant cases of Approach #3 is MYbank, an Alibaba’s subsidiary¹⁵.

MYbank, Jack Ma’s US$290bln loan machine, changes Chinese banking by using real-time payment data and a risk-management system that analyses more than 30,000 variables. The financial-technology boom that turn China into the world’s biggest market for electronic payments is now changing how banks interact with companies that drive most of the nation’s economic growth.

This 4-year-old bank lends 2 trillion yuan to nearly 16 million small companies and consumers. Borrowers apply using a few taps on a smartphone and receive cash almost instantly if they are approved. The whole process takes 3 minutes and involve zero human banker. The default rate so far is about 1%.

The biggest data sources may come from payment providers in Alibaba Group like Ant Financial, the largest shareholder of MYbank. After obtaining consents from borrowers MYbank analyses real-time transactions to gain insight creditworthiness. For example, a drop in customer payments at a retailer’s flagship store might be an early indicator that the company’s prospects and ability to repay debt are deteriorating.

Another unique source is from government-administered ‘social credit system’, Chinese unique scoring which is being tested in various cities across the country as a way to reward good deeds and punish misbehavior. Hence a small business owner whose social credit score dropped because he failed to return borrowed umbrella would find it harder to get a loan.

The upshot of more information is a loan approval rate at MYbank that is four times higher than at traditional lenders, which typically reject 80% of SMEs’ loan requests and take at least 30 days to process applications.

Another case similar to MYbank is Tencent’s WeBank¹⁶.

WeBank, the 1stdigital bank initiated by Tencent in December 2014, ahead of a pilot program granting online lending licenses to non-bank operator in China in 2015. WeBank has built the first ever distributed banking system based on cloud-computing technologies and the blockchain. It has gained critical mass swiftly. In 2018 it reported revenue of 10 billion yuan and net profit of 2.5 billion yuan.

In late 2018, the bank’s valuation passed 147 billion yuan (US$21 billion), making it one of the world’s largest unicorns; this is not a public valuation but was gleaned from an auction notice for a minor stake in the company, posted on Taobao.com. As of the middle of this year, the NPL ratio is thought to be around the 1%.

In addition to the Alibaba and Tencent-backed internet banks, Baidu is a backer of aiBank, alongside China Citic Bank. But WeBank is, so far, the leader in assets, loans, net profits, return on equity and non-performing loans.