The increasing uptake of artificial intelligence (AI) by the financial services industry in Asia Pacific (APAC) is set to change the way companies acquire and engage customers, as well as reduce risk. Advances in deep learning help organizations gain a more holistic view of potential customers, and provide better judgement as to their suitability for finance, such as credit cards and loans.
With revenue of approximately US$1.6 trillion and profits of about US$700 billion in 2018, the banking industry in APAC accounted for 37 percent of the world’s banking pre-tax profit . Trends show, however, signs of decelerating growth, tapering margins and the need to scale to boost performance and remain competitive.
The good news is that the APAC credit card and loan market has great potential to grow. Despite low penetration in many APAC countries , the region accounts for about half of global credit card expenditure . This makes customer acquisition for credit card products a clear path to future profit.
In the meantime, the increasing adoption of AI tools in finance will see AI become a key player in customer acquisition, combining marketing savvy with sharpened risk assessment thanks to deep learning, as banks and financial institutions, including peer-to-peer (P2P) lenders, seek to grow their portfolios through credit cards and loan products.
After retail, banking will be the second largest industry to invest in AI , with US$5.6 billion expected to go towards solutions including threat prevention and fraud analysis.
Countries such as China and South Korea have already paved the way with their AI initiatives. Chinese fintech companies are targeting less advanced Southeast Asian markets , such as Indonesia, Vietnam and the Philippines, by deploying AI to acquire P2P lending customers as well as manage credit risk and assure compliance for debt c ollection. Meanwhile, in 2018, the government of South Korea pledged to invest US$2 billion into AI research and development by 2022.
Be Selective, Be Precise
The role of AI in customer acquisition and engagement is twofold. Financial organizations can use AI’s deep learning capabilities to gain a holistic picture of their target audience. The advantage of this is that the more data the AI has, the better its predictions and decisions.
Combine your historical customer data using a data science platform, such as transactions, previous campaign results and past customer activities on-site, and then let AI sift through all the data to analyze user behavior across multiple devices and draw a complete picture of your customers. Such insight would help you target them at the right time on the device most likely to give the best response.
To dig deeper, you can add third-party data and use deep learning to discover customers’ interests and intent not only on your own channels, but also on external websites. This will help you pinpoint their life stage. For example, a retail bank may seek career women in their 20s who are browsing and investing in better quality office wear, whereas a P2P lender may like to target customers who are showing signs of entrepreneurialism in emerging economies.
With such knowledge, you can create audience segments based on desirable traits with regard to interests, intent and life stage. Deep learning can then predict future conversion rates and return on advertising spend of these segments to identify which ones are most likely to commit to a credit card or loan product.
These customer segments will also lend insight to personalize your campaigns and create ads, in order to drive higher engagement. For instance, Andy just paid off his car loan, and he has been reading articles about buying new apartments and property tax. You might consider him as a potential customer for a home loan in the near future. So, a content piece with relevant information through email or an ad with the best interest rate for home loans through web push will likely trigger customer engagement.
In addition, AI can also contribute to ongoing customer engagement and retention. By tracking customer engagement with and reactions to previous campaigns, it can identify the best time to deploy a campaign, the most effective marketing channels – whether that is an app push notification, an EDM or web push – and the creative material most likely to elicit a favorable response.
Finding the most valuable customers for your credit card or loan products is one thing, but preventing fraud is equally important. AI tools can provide cost-saving measures in the risk management and fraud detection department. Finance companies are already using AI’s ability to process and analyze large amounts of data to find patterns that can point to wrongdoing , such as flagging anomalies in transactions that might signal fraud.
AI can also support customer acquisition by assessing risk even before marketing to potential customers. In a traditional assessment, credit card and loan providers would evaluate elements such as income, existing debt and repayment history, but AI can add further perspective by using supplementary data, from spending habits and lifestyle to property ownership, insurance claims and other personal indicators, to contribute to an assessment of the customer’s risk level.
The financial services industry is a data-heavy sector, which makes it ripe for the benefits and cost-savings AI investment will bring. AI’s ability to analyze data and predict behavior sees it as an asset in both the customer engagement space and as part of risk assessment and threat prevention. As fintech investment matures, don’t be surprised to see a significant uptake in AI processes and solutions to make this industry in Asia Pacific more efficient and effective, and help it scale to an even more competitive size.
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