New Zealand
New Zealand New Zealand
Consumers make most of their payments by internet banking
  • 74%
  • 70.5%
  • 54.5%
  • 46.5%
  • 39.6%
  • 40.7%
  • A higher percentage make payments via internet banking to banks and insurance companies, telcos, and retailers, respectively, compared to the regional average
  • Impact: Anti-fraud capabilities critical to the increased digital transaction frequency and customers’ trust in banks
Australia Australia
Consumers are most satisfied with the post-fraud service of banks and insurances companies
  • More than 70% satisfaction rate compared to 59.7% on average
  • Impact: Increased trust in BFSIs
Indonesia Indonesia
Consumers that encountered most fraud incidents in the past 12 months

AP Average

  • 49.8% have experienced fraud at least once compared to 34.7% on average
  • Impact: Overall anti-fraud capabilities need improvement
Singapore Singapore
Consumers have the highest trust towards government
AP Average
  • 75.5% choose government agencies, compared with 51.7% on average
  • Impact: Trust of personal data protection is centered around government agencies
Vietnam Vietnam
Consumers encountered most fraud incidents in retail and telco during the past 12 months
  • 55%
  • 54.5%
  • 32.8%
  • 35.2%
  • 55% and 54.5% have experienced fraud at least once in retail and telco, respectively, compared to 32.8% and 35.2% on average
  • Impact: Overall anti-fraud capabilities need improvement
Thailand Thailand
Most Thai consumers believe speed and resolution are severely lacking (response/ detection speed toward fraud incidents)
AP Average
  • 60.5% think it is most important, compared to 47.7% on average
  • Impact: Response time as one of key factors to fraud management to retain customers and gain their trust
India India as standalone
Consumers have the largest number of shopping app accounts in the region
  • Average of three accounts per person
  • Impact: Highest exposure to online fraud
Hong Kong
Hong Kong Hong Kong
The least percentage of consumers with high satisfaction level toward banks and insurance companies’ fraud management
AP Average
  • Only 9.7% are most satisfied compared to 21.1% on average
  • Impact: effective response towards fraud incidents to be improved
China China
Consumers are the most tolerant toward submitting and sharing of personal data
AP Average
  • 46.6% compared to the AP average of 27.5% are accepting of sharing personal data of existing accounts with other business entities
  • Impact: higher exposure of data privacy and risk of fraud
Japan Japan as standalone
Consumers most cautious on digital accounts and transactions
50.7% Actively maintain digital accounts’ validity
27% AP Average
45.5% Do not do online bank transfers
13.5% AP Average
  • More than 70% did not encounter fraud incidents in past 12 months, compared to 50% on average
  • Impact: Relatively low risk of fraud

Artificial Intelligence: Elevating the customer banking experience

Artificial Intelligence: Elevating the customer banking experience

Amidst a fast-paced and flourishing consumer financial landscape, many critical processes remain inherently ‘broken’ – inefficient, time-consuming, and labour-intensive – thereby limiting market growth. For instance, mortgage applicants today often expect the entire cycle of approval, from furnishing the required legal documents to signing the final contracts, to take weeks, or even months.


However, recent developments in Artificial Intelligence (AI) are poised to accelerate such processes — in particular, financial decisions that deal with large volumes of consumer data.


Broadly speaking, AI can determine, locate, and analyse data sets to simplify complex financial decisions and produce fast and simple answers for customers. This element of computer intelligence has tremendous potential to be the catalyst to push consumer finance to the forefront of serious transformation and growth.


Industry shifts fuelling the rise of AI


Indeed, in today’s world of immediate results, lengthy application forms and week-long waiting times should no longer be the business norm. But for some financial institutions, it is difficult to shake off legacy banking processes, in part due to complex regulatory demands and lack of modernisation resources amongst others. This causes significant friction in the customer experience, which can affect the revenue stream in the long run as consumers switch to more user-friendly banking platforms.


However, prominent shifts within the industry, such as data growth and cheaper computing resource, have contributed to very positive changes in consumer finance over the last decade, in particular, the rise in AI adoption. The pace of change is further accelerated by the need to keep up with new customers’ needs, creating a fertile ground for AI applications to thrive.


Putting these elements in context, almost half of the world’s 1.7 billion unbanked population comes from eight countries in Asia Pacific. However, telecom, e-commerce and e-wallet growth have together created significant data on this group of people. In parallel, Moore’s law has brought down the cost of data storage and computing exponentially over the last decade. The time is ripe for AI toolkits to inject more insights into the industry.


The outcome of this shift in banking technology can be tremendous. Going beyond the front-end business operations, AI can be applied to backend IT architecture to enhance capabilities to process complex tasks such as deal originations, credit reviews, or risk assessments.


AI applications in action


Today, there are many ways that financial institutions can glean actionable insights from computers, depending on the data models, algorithms, and methodologies deployed.


With supervised learning, the most common approach to machine learning today, computers are taught to provide the intelligence and recommendations human workers need to make decisions. Data scientists can improve these models over time by continuing to ‘feed’ the machines new and defined datasets, not unlike a human brain creating a virtuous cycle of learning.


Supervised machine learning plays a critical role in allowing financial institutions to create a compelling banking experience. Going back to the mortgage application example, once the application is submitted, machines will quickly locate and examine the applicant’s financial history in order to make an informed prediction on his or her credit performance – without human involvement.


Detecting financial fraud is another practical application of machine learning. Compared to older fraud detection models, AI models excel at segregating good credit from bad credit using much richer variable sets. They would also be able to continually look at these variables and find developing relationships that could point to abnormalities.


While the scenarios we have outlined above work very well in theory, there are still a few issues to be ironed out as we move towards the large-scale application of AI in consumer credit decisioning.


Explainability – explain decisioning better


The consumer credit model we see today is a reasonably transparent system on which the consumer has been educated over decades. The average consumer typically understands the kind of behaviour that impacts their credit standing and knows their rights to request for personal information held by the banking system.


But as machines become more involved in decision-making, many fear that there will be a lack of understanding and clarity regarding credit decisions. Fortunately, in parallel with the rise of AI applications in finance, various approaches to improving AI’s ‘explainability’ and eliminating machine bias have also flourished in recent years.


Data scientists tighten control over AI’s data sources by setting up processes to eliminate certain variables in machine decisioning that can introduce or perpetuate bias. With the right algorithms, AI-powered credit systems can even significantly reduce human’s subjective interpretation of data, allowing decision-making to be fast, neutral and accurate. From a consumer perspective, there is indeed much to be gained from the application of AI within the finance sector.


As the traditional banking model gives way to the next wave of transformation, legacy processes will soon become a thing of the past. Digital banking paradigms are poised to deliver personalised interactions, speedy services, and relevant products – all the hallmarks of the next phase of consumer banking. AI will be a key catalyst of this shift, powering an intelligent ecosystem that constantly refines and reshapes the future customers’ banking experience.


Mohan Jayaraman
Managing Director, Innovation & Strategy, Experian Asia Pacific


Read full article

Mohan Jayaraman

By Mohan Jayaraman 02/19/2020

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