Computing advances and data expansion have suddenly made AI part of everyday life and an invaluable tool for almost every industry. Healthcare, manufacturing, transportation, law enforcement, national defense, and education all stand on the precipice of revolutions due to AI’s evolution – but perhaps no field is so perfectly suited to incorporate its potential as financial services.

Artificial intelligence now allows institutions to fully assess a wide array of available data with analytical/predictive algorithms that provide insight and solutions for fraud prevention, cyber-security, lead generation, and most notably, investment operations.

Automated robo-advisers already have about $3 trillion in assets under management, and that figure is expected to hit $16 trillion by 2025. But perhaps the most fertile niche for AI expansion is personal finance.

Standing at the nexus of consumer expectation and emerging tech abilities, AI-driven personal financial services will supply tailored products, customized advice, and 24/7 service to individual clients, driving expanded bank business and democratization of the investor class. Let’s see how this aspect of fintech is taking shape, what it means, and how it will be implemented.

Defining the AI fintech revolution

Since the days of the abacus, no domain has so heavily relied on crunching numbers as the financial industry. From actuarial tables to demand curves to P/E ratios, empirical data has driven investment and shaped the global economy. But today’s explosion of available information and the means to dissect it has rendered most business intelligence analytics obsolete.

Rather than looking backward at institutional efforts to determine what worked in the past, AI offers the opportunity to expand data sets exponentially, analyze limitless individual elements, and generate algorithms that can interactively monitor all operations in real-time while running millions of predictive/reactive models to guide market choices.

These advances promise to enhance security, optimize operations, and improve customer service. This is why 80% of banks highly anticipate fintech’s AI advantage. But how will they put it to work?

AI financial applications

Incorporating emerging technology is nothing new to the financial sector; electronic transfers, credit card networks, SWIFT codes, digital trading, and ATMs were all cutting-edge when adopted.

More recently, consumers have moved to online banking, and online accounting has proven to be one of the most important strategies for simplifying small business operations. Yet, no previous technology advance held the potential to reimagine so many aspects of banking as artificial intelligence.

By harnessing the power of processors and data, AI streamlines repetitive processes, automates tasks, prepares for infinite possibilities, and is equipped to handle fluctuations in ways well beyond pre-programmed possibilities. These toolsets, in turn, facilitate anomaly detection, opportunity anticipation, and superior customer service that will reshape the industry in several ways:

  • Security Applications: One of AI’s primary strengths is digesting enormous amounts of data to assimilate expectations and root-out patterns overlooked by human analysts. This allows easier identification of fraudulent practices, quick detection of cyberattacks, and automated identification of illicit practices like money laundering. Expansion of such AI methods by the banking industry could save institutions $447 billion by 2023.
  • Personalized Services: This is the richest area for fintech expansion, as it’s not merely an improvement upon current operations but a whole new field enabled by tech advances. With the ability to analyze extensive consumer data and deploy adaptive machine learning that extracts lessons from millions of cases and tailors them to singular situations, artificial intelligence can supply individual consumers with automated financial monitoring, counseling, and investment, as discussed further below.
  • Internal Operations: From baseline improvements like accelerated document processing and timely fact verification to complex algorithmic trading adjustments and the expansion of lead generation fueled by data mining results, AI offers solutions to provide financial institutions greater efficiency and more profits and clients.

The rise of personalized banking

The more banks and financial advisers know about their clients, the better they can customize fiscal planning and products. Today’s interconnected society leaves little unknown about most consumers.

Big data has monitored our behaviors and preferences for years, primarily to separate us from our money with marketing campaigns. Conversely, fintech AI offers the chance to deploy data tools to counsel consumers on how to best save and multiply their assets. Combining market analyses with the deconstruction of personal data and identification of individual goals will allow AI tools to provide expert financial guidance to every segment of the population.

In some smaller ways, that’s happening already. Smartphone apps connected to digital wallets can monitor (and influence) spending habits, investment dashboards for retirement plans let us quickly apportion exposure and diversify investments. According to their individual risk tolerance and investment windows, online trading services incorporate AI-controlled robo-advisors to conduct daily trades for consumers.

Allowing AI to monitor accounts can provide clients with control (and snapshots) of their financial health in real-time: securing assets and credit ratings, paying bills, questioning authorizations, automatically diverting income to college or retirement plans, searching for attractive mortgages or opportunities filtered by unique income, age and risk profiles…all running in the background of simple online banking interfaces. 

Banks can also craft and market custom instruments designed for individual customers based on their history and data, replacing financial advisers just as the Internet usurped travel agents. And it can all be presented and tended by AI-powered natural language chatbots that are on-call 24/7 and have become virtually indistinguishable from humans (but can switch to human operators when “sentiment analysis” detects tension in the conversation).

In short, AI personalized banking provides a suite of wealth management services to everyone, regardless of their wealth. It also does it in a customized manner with minimal pressure, upselling, or human interaction, which is just the way millennials prefer it.

Millennials are now the most populous generation in the country, and are not only coming into their own professionally but are about to come into a great deal of inherited wealth. They’re going to need help investing it, they trust technology, and they expect personalized treatment (from food delivery suggestions to curated playlists, movie recommendations, and social media feeds).

Individualized banking options powered by artificial intelligence analysis look to be an ideal solution and for both financial institutions and their clients.


Artificial intelligence and machine learning programs leverage data analytics to supply insight, efficiencies, and customization to almost every aspect of modern life. 

Privacy concerns unsettle some, but most enjoy the convenience and personalization enabled by such technology. Already, supply chains anticipate our needs and deliver consumer products the same day. Entertainment platforms present art based on our tastes. Social media selects items based on our interests, and soon, medicine will be designed and administered based on our personal genetics.

The next frontier lies in the financial industry, where banks are currently integrating AI systems to streamline internal operations and are beginning to roll out personalized banking options based upon the same model of consumer customization. Ideally, this development will be one that benefits markets, institutions, and individuals alike by providing stability, liquidity, and equal access across the socio-economic spectrum.

Previous post

Study finds that even the best speech recognition systems exhibit bias

Next post

How data allows TV to be measured the way people watch it