Like most other businesses, banking and financial services organizations are fighting to adapt in this new, disruptive, digital world ” and like most other businesses, big data analytics is at the top of the list of solutions to reign in. While those with the proper expertise and knowledge are finding great opportunity via big data analysis, unfortunately, not everybody is necessarily ready to deploy these solutions. Here’s how big data is changing the banking, finance, and credit industry.
Identity Theft, Credit Fraud, and Data Breaches
Beginning around 2014 or 2015, the world began to understand just how badly malicious actors wanted to profit from data breaches ” as well as just how far these actors would go. According to the experts at University of Illinois Chicago, more than 750 data breaches occurred in 2015, the top seven of which opened over 193 million personal records to fraud and identity theft.
While this first spate of cyberattacks generally targeted healthcare data, criminals also began stepping up their initiative to steal and sell credit card numbers on the black market, particularly the dark web. Mobile payments using secure systems have become more popular recently, used by 6 percent of adults in 2013 and rising to 8 percent in 2014 according to Ohio University, but unfortunately, malicious actors, it seems, are more vigilant.
While it’s important to know the difference between identity theft and credit card fraud, the end result is essentially that other people are accessing and using your information to make purchases.
Credit card fraud is closely linked to identity theft, which the Federal Trade Commission recognizes as about 29 percent of consumer complaints, but it is not the same as identity theft, writes personal finance expert Cole Mayer. While credit card fraud can be part of identity theft, they are two very different concepts ¦ Identity thieves can use your personal information, like your Social Security Number or driver’s license, for opening various accounts, from utilities to loans. They apply for government benefits, or try to use the information to forge new documents, like driver’s licenses. They can open up a new credit card account in your name and then use the card, which is a type of credit card fraud that stems from identity theft.
While financial institutions have been forced to step up their security game, not all have been successful. The recent hack of credit reporting agency Equifax has proven that even the vanguards of lifetime information such as Social Security numbers are susceptible to error.
To get a sense of the reach of your credit report, it can be used by a potential employer to determine how responsible you are and can determine the rates on loans for education, cars, homes, and more, writes Olivia Kendall with student loan refinancing company Earnest. Bad credit can make it tough to get certain types of insurance, rent an apartment, and will certainly increase the cost of any loan you take.
It’s up to both consumers and industry professionals alike to shore up security in our future. Here’s how the industry is engaging biometrics, AI, and other security measures to take steps toward a more secure future.
Biometrics, AI, and New Security Measures
One way that institutions are beefing up their security is with biometrics. NJIT defines biometrics as measurement and analysis of unique physical or behavioral characteristics (such as fingerprints or voice patterns), especially as a means to verify personal identity. These features have actually become one of the defining points of Apple’s iPhone X, which uses Face ID to unlock users’ phones.
Even financial institution ING has launched a biometric app for iOS devices users. It doesn’t even require customers to enter a PIN or log into the banking app to do so, writes Sophie Elsworth, all they have to do is ask Siri the simple question, what’s my bank balance?’
According to her reports, a majority of ING customers (69 per cent) said they would be likely to use these types of voice-activated services to perform their daily banking tasks as they become available. It’s not hard to believe either ” the ease with which voice assistants have slid into our homes, phones, and daily routines is accompanied by extremely effective functionality. Unfortunately, there are downsides to these types of security measures.
The problem with biometrics is that they are essentially passwords you can’t change, akin to a Social Security number ” and those suffering from the Equifax hack know all too well how it feels to have unchangeable data compromised. That’s why some financial institutions are instead leveraging AI for security.
Penny Crosman, writing for American Banker, reports that TD Bank is just one of these institutions. The Ontario-based firm just recently acquired AI startup, Layer 6.
The Layer 6 team has built an AI prediction engine that can ingest a variety of data types ” customer profiles, transaction histories, phone calls, images (e.g. photos and documents) and video ” and be trained to make predictions, such as what the next best action for a customer is (e.g., offer her a mortgage), writes Crosman. It can also be applied to detecting fraud, scoping out cybersecurity threats and underwriting loans.
The Good News
Fortunately, big data is not only being used out of crucial necessity and ongoing protection of customer data, but it is also being used worldwide to improve services and customer experience.
Central banks are considering or already collecting data that is transaction by transaction, trade by trade, asset by asset, mortgage by mortgage, loan by loan,” said Maciej Piechocki, Frankfurt-based partner at BearingPoint, in an article with Business Times. “That is allowing them the flexibility necessary to answer policy questions that could not be answered before.”
Listed in the same Business Times article are examples of how big data solutions are being used in central banks around the world:
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JAPAN: The Bank of Japan (BOJ) has been using Big Data since 2013 to analyse economic statistics, starting off by beating private forecasts on the accuracy of its GDP (gross domestic product) predictions and evolving its own experimental index that has pushed the government to assess if it’s understating growth.
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CHINA: The People’s Bank of China in May said that it will increase the use of Big Data, artificial intelligence and cloud computing to boost its ability to recognise, prevent and reduce cross-sector and cross-market financial risks.
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INDONESIA: For a fortnight before a policy announcement, Bank Indonesia’s statistics department scours social media, news sites and other content to monitor public perception and rate expectations. The growth in online shopping means the bank is also now receiving information from big players in the e-commerce market.
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THAILAND: Thailand’s central bank is building its own employment index based on data from online job-search portals and is creating a property indicator to give it a better sense of supply and demand in the housing market.
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EUROZONE: The European Central Bank (ECB) has been exploring Big Data since 2013. Information on some 40,000 daily money market transactions will form the basis of an alternative reference rate as traditional benchmarks become unreliable. It has also bought a large set of prices from actual consumer purchases and is exploring ways to scrape the Internet to measure inflation in real time.
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SWEDEN: Researchers at Sveriges Riksbank showed this year that using data from online retailers could improve the precision of inflation forecasts by providing up-to-date information on goods with volatile prices such as fruit and vegetables.
You can find more examples of central banks in other countries cluing into big data via that Business Times article here.
Big data in finance has simply become too big to ignore. Be they private institutions or central banks, the solutions provided by analytics are becoming necessary for anybody operating in the modern world ” for better or worse.
