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.

Conclusion

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.

No longer considered the “future of work,” AI is infiltrating industries and job roles at impactful rates.

Across the U.S. economy, one-third of the U.S. workers are interacting with some form of AI in their jobs today – even if many of these interactions are still in a limited capacity. And although much attention is directed toward industries that are easy to visualize (think driverless cars or robots on factory floors), the data indicates that the financial services industry may see the greatest change.

Financial services is already an advanced adopter of AI; it ranks third behind only the information industry – which includes the software subsector –  and the manufacturing industry in terms of the percentage of workers exposed to AI, and it dwarfs U.S. averages in both breadth and depth of AI adoption. (See the table below for AI exposure rates.)

In the coming 12 months the financial services industry will outpace average U.S. investment in AI by more than 50%; the securities, commodities & investments subsector will invest at twice the average. But the rate of investment is not the only thing that makes the financial services industry unique when it comes to AI.

Portion of U.S Financial Services Workers Using or Exposed to AI by Subsector, 2018

Financial services is different because AI can affect all three elements of its revenue model. AI is effective at generating new fi-serv business, effective at automating back-office processes, and in many cases – as with programmatic trading or portfolio management – AI is the product.

An “AI Preparedness Survey” explored this difference by asking workers about very specific tasks they performed in their jobs. The data showed that a greater percentage of fi-serv employees’ weekly work hours could be eliminated via automation (15.0%) than of those for workers in the manufacturing industry (12.0%) or in the professional services industry (12.6%).

Financial services workers are seeing these effects through the deployment of specific technologies. For example, in the consumer & business banking subsector, the deployment of voice recognition technology to automate facets of customer interaction nearly doubles the deployment of that technology in U.S. industry at large. Not surprisingly, the data shows an even greater deployment disparity for programmatic trading tools in the securities, commodities & investments subsector. Injecting automation into customer service tasks and transaction processing may be seen as essential progress that frees up valuable human capital to perform more complex tasks, but it should be noted that the financial services industry’s deployment of scenario-planning AI – i.e., “thinking software” – is also quite accelerated (more than 10% of fi-serv firms will deploy scenario planning tools this year, versus only 5.9% of U.S. firms in general).

Source: Optimized Workforce, 2018

What does all this mean from a talent strategy perspective? The financial services workforce – and the tasks the people in it perform – are likely to experience more change than in other sectors of the U.S. economy. Financial services leaders will need to perform very granular task-based audits and skills assessments to understand and redefine their company’s talent needs. And workers in the industry should be aware that the talent pool the financial services industry will need following these audits may get smaller.

When these changes will take place is hard to say. Nearly 20% of financial services employees today report spending so much time on tasks AI could automate that they are missing key business goals – an indication that in today’s economy, the demand for financial services labor is still quite strong (a finding also supported by the current unemployment rate). The timeframe is also dependent on when fi-serv leadership begins reassessing their workforce in earnest.

Note: The article content is inspired by a recent report by Optimized Workforce.

Many marketers representing mid-market financial services companies labor under the impression that due to their size and scope, the data-driven marketing tactics used by the dominant players are simply out of reach for them.

This is a shame and quite far from the truth. Data, analytics, technology and the overall process that supports data-driven marketing have undergone significant (dare I say radical?) transformation over the past decade. The result is a great democratization of marketing tactics. Today’s mid-size financial services organizations can now execute marketing initiatives that rival those of banking giants.

The first step in encouraging more marketers from mid-size organizations to consider data-driven marketing is to debunk the misconceptions I hear most frequently.

Misconception #1: Due to legal requirements banks can only do broad-base advertising, such as billboards, radio spots and print ads

While it’s true that Federal regulations ban advertising that targets consumers based on age, ethnicity, income and other factors, marketers still have plenty of data options they can use to identify their ideal prospects, both online and off.

Marketers can leverage a wide variety of online and offline behavioral data to model your best existing customers. Once you know the online behaviors of your best checking account customers, those insights can be applied  to find similar consumers — entirely new to your bank – who exhibit similar behaviors and target them with addressable advertising on their mobile devices, desktop computers and mailboxes.

Misconception #2: Data-driven marketing won’t help me form relationships with consumers (they need to come into a branch for that to happen)

It’s true that many consumers, millennials in particular, want to be educated about financial services products, and are keen to understand how a bank and its products will fit into their lives and their communities. You can use this desire to begin the relationship building in your data-driven marketin perspective.

Take advertising as an example; it’s not enough to state the interest rates your charge for a home equity loan, consumers want to know how your bank will help them remodel their home after the birth of a new child. With data-driven marketing, you can target consumers who live within your designated market area (DMA) and who purchase diapers or visit sites for new parents.

Misconception #3: You need a database and a huge budget to do data-driven marketing

While the big banks certainly maintain huge databases in house to support their marketing initiatives, mid-size institutions can leverage a virtual datamart for campaigns. These SaaS-based solutions host your first-party data (securely and privately), and provide mechanisms that let you integrate it with a huge array rich third-party data, both online and off.

Datamarts allow mid-size marketers to accomplish several critical tasks. First, you can associate online user IDs with offline data (thus allowing you to send a direct mail offer to a household in which a member visits a website for new parents!). It will allow you to gain insight into your current customers, including interests, intents and other psycho-demograpic information. Most importantly, it will allow you to build customer models to use to target new customers to your bank.

Though this may all sound expensive, a datamart will shave up to 90% of the costs of relying on an in-house database.

Misconception #4: Maybe data-drive marketing can help me reach the right person, but it can’t help me achieve my goals

Merchant John Wanamaker famously said, “Half the money I spend on advertising is wasted; the trouble is I don’t know which half.” But that was long before the advent of data-driven marketing! The advances in data and tracking now allow marketers to optimize campaigns based on specific business metrics, such as number of accounts opened, cards applied for and deposits made.

It all begins with an assumption: Based on my data-driven customer models, I assume that this type of consumer, under these conditions, will open a new checking account. The marketing execution platform targets consumers who fit the model, and tracks the results. If conversions occur as anticipated, targeting criteria remains intact; if they don’t, the model makes adjustments and measures the results. These models are designed to focus campaign spend on the scenarios that drive the most business KPIs.

Misconception #5: It takes way too much time to embark on data-driven marketing

A lot of marketers I talk to believe that data-driven marketing is a huge endeavor that requires a 12- to 18-month lead time. The truth: thousands of mid-size organizations, both in and out of the financial services sector, create models, design offers and execute campaigns within 30 days using a datamart described above.

Keep in mind that although it may take just a few weeks to design and execute a campaign, all models need time to learn. In my experience, models that are given at least 90 days tend to deliver the best results (but since campaigns tend to have multi-quarter flight dates, this isn’t a problem).

While your particular path and level of marketing maturity may differ, taking advantage of the trickle-down effect with marketing and organizing your efforts around the modern consumer first will yield far better results than remaining married to theses misconsceptions.

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SAN DIEGO, CA – Dealstruck, Inc, selected as the exclusive crowdlending partner to demo at FinovateSpring 2014, April 29-30 in San Jose, today unveiled two new products.

The first product is an application programming interface (API) for institutional investors, which provides them with the raw data for every deal presented on the Dealstruck platform. This data allows investors to use their own underwriting methodologies to determine how best to build out their portfolios.

The second product is an enhanced portal for accredited investors, providing them with access to detailed underwriting data used by Dealstruck so they can make investment decisions in real-time and with complete transparency. As borrowers upload documentation and progress through underwriting, investors can see the data and decision-making as it occurs.

“While Dealstruck has been originating loans for nearly a year, and has provided basic information to our lenders, today we are excited to introduce complete transparency to our crowdlending partners,” said Ethan Senturia, CEO. “For the first time, investors have access to all of the data they desire to empower them with complete confidence in their investment decisions.”

Institutional and accredited investors can easily begin to review deals by visiting https://www.dealstruck.com/.

Dealstruck is one of 70 companies that received an opportunity to present for seven minutes to banking and financial institution executives, venture capitalists, members of the press and entrepreneurs at Finovate. To schedule an appointment to meet with Dealstruck at the event, please contact info@dealstruck.com.

About Dealstruck
The Dealstruck lending marketplace connects profitable, small- and medium-sized businesses (SMBs) with innovative credit solutions funded by individual and institutional accredited investors. Unlike the one-size-fits-all approach offered to them by banks and the high-cost, short-term credit offered to them by alternative lenders, Dealstruck provides growing SMBs with a suite of products that give them a credible and transparent path to bankable. Dealstruck is the first crowdlending platform to offer multiple products to SMBs, and the first to allow investors the freedom to choose specific investments. For more information, please visithttps://www.dealstruck.com/.