No industry has completely avoided technology-driven disruption over the past 25 years, but some have certainly undergone greater transformations than others. The banking business, for example, is one industry that, at its core, is largely unchanged.
In 2016, that could start to change though thanks to three powerful technologies poised to reshape how financial business is done around the world.
Forget bitcoin. The block chain is the real revolution.
You've probably heard of bitcoin, but you may not be familiar with the technology that allows bitcoin to exist: the block chain.
The block chain is the public ledger that records every bitcoin transaction around the world. It is a distributed network across the Internet that uses a system of mathematically verified keys, or signatures, to ensure bitcoins are transferred from and into the correct accounts. The keys ensure no transactions can be altered after the fact, and that balances are updated and correct before and after each transaction. The distributed network means no single entity has control of the data, increasing transparency and trust.
Using block chain as the foundation, a whole host of financial processes and products are now being reimagined. Consider payment processing.
Today, when you swipe your credit or debit card, your bank authorizes the transaction, the merchant's bank authorizes receiving the transaction, the merchant receives the payment, and a payment processing network facilitates the transfer. This process works, but it's also slow, inefficient, and expensive. Visa (V -0.59%), the leading payment processing company, generated $13.9 billion in revenue in 2015 alone, with virtually all of that coming from fees collected on electronic payments just like this example.
In a system built on block chain technology, the transfer of money could be immediate, secure, and free. You wouldn't just eliminate the middleman; you'd eliminate all three of them!
The potential for block chain doesn't stop there, either. Block chain technology could be applied to a whole host of transaction scenarios, from legal contracts to buying or selling a house, accepting a loan, or even buying stock.
With the potential for disruption so great, many large companies are investing today to get ahead of the curve tomorrow. JPMorgan Chase (JPM 2.51%) and IBM (IBM 0.06%) teamed up last year with several other large financial and technology players to create the Open Ledger Foundation. The goal of the foundation is to take existing block chain software and innovate on it to create the systems needed to bring this technology mainstream. The commitment to block chain is reflective of the financial capital these companies are putting into its development. JPMorgan, for example, is reported to be budgeting upwards of $9 billion to its development of block chain and other next-generation technology.
Combating cybercrime with tokenization
Cybersecurity is arguably the biggest threat to the financial system today. The widespread adoption of tokenization technology is the latest major change to how we exchange money electronically, and it's all about improving the security of your data.
Tokenization is a process by which small packets of data, or tokens, are used in electronic transactions instead of a direct connection to your accounts. The tokens contain only the data needed to complete the transaction and nothing more, limiting the exposure of your information. In other words, if a hacker gains access to the transaction, they will only steal a small packet of data that loses its usefulness once the transaction is complete. Key information, like your account number, is kept hidden and out of reach to any potential hacker.
Tokens are effective for more than just credit or debit card transactions as well. Banks and third-party financial technology companies are turning to this technology to provide the interconnectedness consumers demand without sacrificing security.
Late in 2015, consumers lashed out at banks like Wells Fargo (WFC 2.74%) and JPMorgan after the banks cut off third-party apps' access to customer accounts. These apps, like the popular personal finance app Mint.com, used the data from the bank to provide consumers with insights into their finances, improve financial management, and help them budget and save. The banks wanted to protect consumer data, but consumers demanded the services offered by these third parties.
The solution was tokens. Instead of granting full access to the accounts, the banks now provide third parties with tokens that provide limited information to both protect customer data and still allow for the third-party services to function.
Making sense of big data with machine learning and artificial intelligence
Banks collect huge amounts of data on their customers. How much they spend, where and on what, personal information, and more. All of that data can be incredibly valuable if banks could just unlock the secrets it holds. In 2016, they should be able to do just that.
The reason why is new statistical capabilities that can use all of that data and apply artificial intelligence to improve aspects all over a bank's operations. Artificial intelligence, or machine learning, consists of advanced algorithms that analyze large chunks of data and, over time, learn to recognize cause and effects within the data.
For example, banks are already using machine learning to prevent and stop identity theft. Specialized software reviews millions of transactions from debit and credit cards, attempting to determine what a fraudulent charge looks like compared to a legitimate charge. Once complete, the bank feeds customer transactions into the algorithm in real time. The algorithm can instantaneously predict if that transaction is likely to be fraudulent or not. If the algorithm is confident enough, that transaction could be declined, saving the bank money and protecting a consumer's identity.
A similar application of this technology was behind IBM's successful Jeopardy appearance, where its artificial intelligence computer, Watson, defeated two of the game show's greatest previous champions. The software interpreted the question, referenced its database of information, and returned an answer in the form of a question.
Like Watson, banks have lots of available data points on lots of different things. With machine learning, that data can inform management decisions, drive automation, create efficiency, and lower risk.
Machine learning can be applied to risk management to improve credit decisions. It can be used to predict optimal cross-selling opportunities for every customer in the bank. It's being used to trade stocks, bonds, and commodities.
It can even be used to automate many customer service functions. USAA, for example, is already using IBM's Watson to automatically answer customer questions online. The sky is truly the limit.
