Cracking the Asian banks' big data conundrum
How can banks improve the clients' banking experience without compromising data security?
When Hong Kong millennials were asked to provide information to financial services companies, 7 out of 10 were reluctant to do so due to a fear that their identities will be stolen through their online, mobile, and app-based activities. This represents one of the emerging challenges for Asian banks that want to leverage big data: How do you collect valuable and often highly personal information from customers – information critical to prevent identity fraud and other financial crimes as well as improve banking experience – while assuring them that their data will be protected?
Solving such conundrums should be a top priority for Asian banks that want to use big data analytics to woo customers in increasingly powerful ways, according to industry analysts. Banks that are successful will be able to deliver impeccably smooth transactions, provide offers before customers even know they need them, and ensure customer loyalty amidst an intensely competitive field.
Seamless customer experience
For many Asian banks, big data analytics presents an excellent opportunity to create a deeper yet more seamless customer experience. This is a trickier task than it seems since customers demand increased convenience and better data protection, but the two can sometimes be at odds with each other.
“A natural conflict exists – companies, like banks, need specific types of information to make the customer experience frictionless and financial crime-free, yet consumers hesitate to share the necessary information,” says Mike Shaw, vice president, global market development at LexisNexis Risk Solutions, citing the study on Hong Kong millennials.
“One way to overcome this challenge and optimise bank operations is for Asian banks to come together and form a consortium that enables them to perform customer onboarding screening, for example, in a more efficient manner.”
Shaw says a customer due diligence consortium currently does not exist in Asia, and because of this, banks are not capturing know your customer (KYC) learnings from peer banks. Customer onboarding also remains highly inefficient in the region, especially for clients that must open accounts and transact with multiple banks.
“Each bank asks for the same documents and information and screen the same small business customer multiple times. The time to get the small business onboarded becomes lengthy and frustrating for all involved,” says Shaw.
“When a bank consortium exists, though, that small business customer could be screened only once and all the banks in the consortium would have access to the same information about that small business. This logical method of data sharing makes banks more efficient and creates a much more desirable customer experience,” he adds.
Asian banks, Shaw argues, can take a cue from the United States where the Small Business Financial Exchange Inc. has managed to gather 200 members, including the largest lenders, to share data and make more informed decisions.
Asian banks are also embracing big data and advanced predictive analytics to speed up banking processes, boost fraud detection and generate insights that will enrich the customer experience.
Around 82% of banks, retailers and billing organisations in the Asia Pacific region wish to increase investments in payment systems in the face of increasing competitive pressure for faster processing, says Giselle Lindley, APAC solution and fraud consultants leader, payments risk solutions & big data at ACI Worldwide, in the latest research the payments systems company conducted with Ovum.
“As the world quickly shifts to immediate payments, it is more pertinent than ever to match seamless customer experiences with responsive security measures. When security measures are too general or conservative, some genuine transactions are prevented and unintendedly result in customers having to produce further authentication or being falsely declined. False fraud detection ultimately leads to poor service and dissatisfaction for genuine customers,” she adds.
Lindley reckons that predictive fraud analytics assists in this area, identifying real-time fraud by sifting through huge volumes of both financial and non-financial data. Thus, Asian banks ensure that their fraud detection solutions accurately allow genuine transactions to occur, without overly inconveniencing customers who want their payments quick and frictionless.
Aside from preventing fraud, predictive analytics unlocks a treasure trove of insights for Asian banks with which to improve and personalise their products and processes.
“By analysing individual customer experience, preferences, and satisfaction data along with the customer’s actual usage and demographics, predictive analytics enables banks to offer personalised services to their customers,” says Anna Gong, CEO at Perx. “When combined with artificial intelligence, predictive analytics can further help banks learn and understand user behaviour and patterns and thus define their product strategy accordingly.”
Asian banks might be lured into thinking that the success of big data initiatives lies in how much meaningful data is captured. But this step is only a prerequisite for a more important action: Generating actionable insights.
“Capturing meaningful data is however only half the battle won – the real challenge lies in interpreting the data to extract relevant and actionable insights,” says Luca Zuccoli, head of analytics for APAC at Experian, an information services company. “Data must be analysed across the banks’ operations, rather than in isolation, in order to make informed decisions.”
“It is imperative for banks to take a more proactive approach to data management, rather than a reactive one, to drive improved customer experience and robust operations,” he adds.
When used proactively, predictive analytics grant Asian banks the ability to better target marketing offers, and predict fraudulent, says Anneliese Schulz, vice president at Software AG Asia. But gaining access to the right data at the right time can be a headache for some banks due to the plethora of channels and applications from which they need to extract. Asian banks must also grapple with making sense of enormous streams of data and acting on it in real-time, sometimes in as short as a fraction of a second.
“Banks in Asia require the ability to analyse huge volumes of data in a short period of time coupled with automation of actions and predictions,” says Schulz. “While deploying off-the-shelf analytics solution may seem like an easier option, the solution might not be able to manage the huge volumes of data or provide banks with the flexibility to set and change analytics rules as required.”
It does not help that some banks are implementing multiple but isolated software and platforms to collect and manage customer information and interactions.
“These platforms are often not integrated or able to communicate with each other. As such, these organisations are stuck with voluminous amounts of data from multiple data sources, but are limited in being able to analyse this data, and even more so in being able to act upon it,” says Erich Gerber, general manager, Asia Pacific and Japan (APJ) at TIBCO Software.
“While a majority of Asian banks have already built the infrastructure necessary to handle securely raw data, the main challenge they now face is extracting value from all that data. Without the tools to extract and load the data into analytics platforms, the value from big data analytics is not realised. This leads to an inability to cater to their consumers, especially to their retail customers, in a personalised and context-relevant manner,” he adds.
Customer-centric data analytics
To avoid costly mistakes in setting up their big data efforts, Martin Häring, chief marketing officer at Misys, advises Asian banks to follow a four-step plan that ensures a more customer-centric approach: Connect, Collect, Communicate and Close.
Häring reckons that banks must first Connect with customers by priming operations to reach the target audience, and focusing on a targeted segment to better understand client requirements.
After this, banks can then move to the second step of Collect. This involves finding and analysing the transactional data that banks have at their fingertips, which now also includes gathering geo-data or location based services, as well as personal finance management data.
Then for the third step, banks should Communicate by telling customers what they need to know, when they need to know. Häring says if a person is saving for a laptop, a bank could send an email offering to provide a small loan, while high-net-worth clients can be introduced to exclusive seminars or webinars on investment management.
The fourth and last step is to Close. Asian banks should implement swift and easy processes when it comes to closing customer transactions. Häringreckons onboarding a customer should take no more than five minutes if their data is already in the system elsewhere in the bank. Digitalisation also aids in this regard, so banks can use instant confirmation through apps or emails to close deals instead of through slow and inefficient paper communications.
In photo (from L-R): Erich Gerber, Anna Gong, Luca Zuccoli, Anneliese Schulz, Martin Häring, Giselle Lindley, Mike Shaw