On a quiet, regular day of trading on April 23, 2013, the Associated Press’ official Twitter account sent out the following tweet:
“Breaking: Two Explosions in the White House and Barack Obama is injured.”
Within seconds, the stock market took a sharp nose-dive and stayed that way for over 10 long minutes. The aftermath: over US$139 billion in losses within minutes from major exchanges and indices in the moments that followed the tweet. The formula: manipulate a popular news channel that is used by artificially intelligent algorithms to determine stock prices, in this case, the AP’s Twitter account.
And while the hacking attempt was thwarted before any more damage could be done, we had witnessed the true power of artificial intelligence.
AI isn’t new to the world of fintech, which is the latest term for the amalgamation of finance and technology sectors. Frequency traders have traditionally used algorithms to evaluate content on the internet that could affect markets prices, which essentially gives them an upper hand when determining share prices. Older AI-based algorithms used to scour the internet for market-sensitive data and apply simple rules and techniques that enable the algorithms to determine the price of a stock.
Newer algorithms are fuelled by smarter self-learning mechanisms that use neural networks to “learn” habits over time, enabling them to become more accurate predictors of behavior and financial trends. These were the algorithms that probably digested the information from the hacked AP Twitter account, sending AI bots into a panicked trading frenzy.
Smarter, faster, and intelligent systems are a dream for fintech. The ability to process troves of data and make complex decisions in a split second is something that is not humanly possible. This is chiefly why AI means so much for fintech, and its beginning to show across different industries.
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Take risk management for instance.
A recent study by the Euromoney Institutional Investor Thought Leadership found that almost half of the financial executives in the survey expected AI to play a significant role in risk management within their institutions over the next few years. This sentiment is echoed across most industries and departments where AI systems are either being tested or fully implemented in HR, finance, and customer relations departments.
Traditional insurance companies often rely on a complicated and time-consuming underwriting process to set premiums for individual customers. In addition to the lengthy process, customers usually have to contend with invasive questions and questionnaires, which makes an already bad experience worse.
AI in Business and Risk Management
AI holds the promise of better marketing and customer service to improved risk assessment across the board. Smart AI bots can be used to scan a customer’s digital footprint on the internet, analyze the data, and make more accurate risk assessments. This way, the customer gets a fair amount of coverage based on their safe driving behaviour.
AI-powered risk management algorithms are also going to play an important role in personal and business financing. When you walk into a traditional bank or lending institution for a loan, one of the first things the loan officer usually does is check your credit score from providers such as Equifax and TransUnion. For years, these scores have been used to determine the creditworthiness of borrowers, usually with varying levels of success.
In many cases, traditional credit scores have locked out borrowers who would have otherwise made good customers. For instance, scoring models don’t take into account Millennials, business owners without bank accounts, and many who don’t bank using traditional financial systems.
AI offers these institutions a platform for better risk assessment, with a good number of fintech startups currently taking up the challenge. For instance, there’s SoFi (short for Social Finance), a startup that uses data from your educational background, career progression, social activity, and other unconventional data sources to make lending decisions.
There’s also PayPal, where you can get a credit decision in milliseconds, thanks to smart AI algorithms that process your eBay shopping history to come up with real-time credit decisions.
And it doesn’t end there. There’re a lot of areas in fintech that will experience the transformative effects of AI, and many that actually depend on machine learning for growth. These will change the way we shop and pay for goods and services online, how we invest, how we bank, and ultimately, how we live out each day in an increasingly digital world.