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

Data analytics to expedite financial inclusion

Data analytics to expedite financial inclusion

The human race is staring at a massive data explosion. By 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet, according to estimates. The scenario as of now, however, is no different as many industries including the banking and financial services continue to pile up high volume, high-velocity information assets known as Big Data. And on an average, organisations use only a fraction of the data they collect and store. The challenge is to decode such capacious raw data sets into strategic and effective insights, improving business prospects. Here, data analytics helps industries and organizations to make more-informed business decisions, using specialized systems and software.


In India, with the Government putting more thrust on financial inclusion as well as adoption of modern methods like mobile banking and online payments, data analytics has become imperative to increase revenue, enhance customer experience, optimize cost structures and manage enterprise risks. Since adopting technology in 2013-14, the Indian financial services sector has seen a proliferation of data sources and technology platforms, challenging the ability of organisations to do justice to customer data. Thus, the adoption of data analytics is key to make banking more convenient, equitable and personalized to user needs. And as we strive to deepen financial inclusion goals, adoption of superior technologies and tools like data analytics would play a major role in managing risks, improving operations and cutting costs.


Let’s look at the benefits of technology and analytics in the grander financial inclusion objective.


#1 Greater risk management

At present, banking technologies deal with three modes of risks; model and systemic, cybersecurity and contagion. Often, operational or systemic risks (within the ambit of an organization) lead to losses from inadequate or failed internal processes, people and systems or from external events (including legal risks). Organisations must have a dependable insight into risks they are managing as well as the effectiveness of controls they have in place. Basel II mandates a focus on operational risks, seeking to identify, measure, evaluate, control and manage risks. Thus, the sector needs a sound operational risk management (ORM) practice. As the threat of new and unfamiliar risks looms, risk-management functions will need a futuristic perspective besides a systemic flexibility to detect and mitigate them. Data analytics can help them identify issues in real time and recalculate risk portfolios within a short period of time.


#2 Customized communication

Banking sector is by default information intensive hence engaging a customer in real-time is key to retention. For example, mobile applications have brought customers closer, improving the quality of overall banking service. Though most of the banks have invested in technology and communication tools, data analytics can sharpen both the message and delivery, applying greater and more contemporary customer insights. It consolidates multiple communication modes into a single and more targeted mean to cut costs and complexities. For example, technologies like chatbots deliver simple tasks real time, allowing relationship managers to focus on more personalised interactions. Banks which are investing in innovative technologies, seeking innovation and disruption, can have their ‘Uber moment’ only if the communication is optimized and delivered through the right mode.


#3 Personalized service offerings

An increasingly competitive environment as well as a dynamic demography are forcing financial services players to rethink customer centric strategies. Banks for example are trying to create more and more commoditized products and solutions, keeping price as a prime differentiator. However, holistic innovations at the customer level can come from smarter solutions which not only help banks acquire new clients or increase the revenue per customer, but also facilitate cross-selling activities, thereby lowering operating costs. Such solutions should be platform agnostic; mobile solutions, for example, now suit both urban and rural areas thereby delivering financial inclusion at a lower cost. Similarly, electronic payments are becoming popular among customers who may now favour a bank that deals far more efficiently through digital means.


#4 Analytics across a customer’s lifecycle

The banking and financial services sector has come a long way from storing basic customer details to creating and analyzing an individual’s progression in the financial space itself. From the `prospecting’ and pre-approvals stage (beginning of a customer’s lifecycle), the data journeys to identify usage of services, retention needs, pre-delinquency till charge off or collection recovery and fraud prospects, creating opportunities and learnings galore for institutions. But with the Internet and mobile devices adding real time data to the customer behaviour, the digital footprint of an individual has dramatically gone up, demanding more powerful computing tools to analyze and interpret the same. Predictability of a customer’s behaviour lies in analysing his lifecycle; from a mere account opening to applying for credit, it all adds up. The credit score, for example, gets developed in the process, helping institutions to accept or reject a loan request or it can convey if a customer is ripe for insurance of any kind. The data crunching - using alternative info such as utility bills, mobile phone bills and usage of credit cards - can further refine the so called `thin-file clients’ into potential targets for various services. 


#5 Customer profiling

As a traditional marketing tool, customer profiling has gained much relevance today in the area of risk policy thanks to a more cutting-edge technological upgradation. Higher quantum of data, greater sophistication of analytics and the availability of multiple channels have sharpened the results, helping banks to profile customers from not only a risk perspective but through an opportunity perspective as well. Big Data tools and techniques help banks detect prospects of fraudulent activities by highlighting the exact challenges, breaking down a customer’s exposure to each product and service, he uses. Key details such as revenue per customer, email responsiveness, product mix and the purchase channels used are examined before segmenting each customer into groups, pegged in terms of risks as well as opportunities. Further, a rich profile of each customer is created to perfect the campaign messaging, channels and specifics (preferred time of the day, etc.)


#6 Market insights

Market insights from data analytics can significantly improve an organization’s basic capability to define, revamp and change their business strategy, using an overview of the market, portfolio quality, sourcing quality, segment specific reports and geographical trends. The depth of the analysis can be as profound as possible as for example, the portfolio quality reports can delve deep into vintage composition and curves, delinquency reports and collection flow reports, etc., enabling institutions to refine service standards and strategies to be deployed.


By 2020, the median age of Indians will be 29 years – an ultra-young population with access to high speed Internet and real-time services. They will inspire an explosion in the customer base, innovation in products and services and the introduction of newer platforms - which will require massive investments on behalf of banks and financial services companies. Here, Big Data analytics can transform the way financial services are designed; data mining can customise financial services from a perspective of utility and suitability besides targeting right customers with a more appropriate communication.


Contributed by Mr. Mohan Jayaraman, Managing Director, Experian Credit Bureau, India

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Mohan Jayaraman

By Mohan Jayaraman

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