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Artificial Intelligence Reaches the Banking Mainstream

After many years as a “science project,” Artificial
Intelligence (AI) is delivering real value for financial institutions of all
sizes.

AI has long been a hot topic in financial
services circles, for a variety of reasons. Banking activity generates reams of
transactional data, precisely the type of information needed to power AI
engines. Banking clients-both consumers and small businesses-crave the
additional insights and advice such data analysis can generate. And financial
institutions of all sizes- not to mention non-bank market disruptors- are keen
to generate added value and loyalty by delivering these insights.

As recently as a few years ago, such AI
initiatives were relegated to lab projects with data scientists toiling away
far removed from day-to-day operations, aiming to prove the concept’s potential.
Only the largest national banks had the R&D budgets to afford such projects,
investing in hopes of creating a “first mover advantage” and a leg up over their
competitors.

Several of these experiments panned out-leading
to high-profile products and features like Bank of America’s Erica, Capital
One’s Eno, and Wells Fargo’s Control Tower. Given the modern technology curve,
however, AI is no longer the exclusive domain of the megabanks. With the help
of fintech partners, community banks and credit unions are already harnessing
the technology to generate immediate ROI with both existing and new customers.

Predictive modeling, a common application of
AI, has many uses within a bank setting. Credit scoring is perhaps the best
known and most established use of predictive modeling-after all, its entire
purpose is to determine the likelihood that a loan will be repaid. AI has
proven quite effective in identifying previously unnoticed patterns in repayment
behavior, bringing greater sophistication and accuracy to these models.

AI has also enhanced the effectiveness of front-line
associates, who can now be prompted with better matched product recommendations
for prospective clients, as well as more rapidly retrieve relevant data to
resolve queries. Customer experience for inbound contact center support has
also been enhanced by AI, which powers chatbots to replace conventional IVRs in
a variety of self-service scenarios. These capabilities can be delivered
through either voice recognition or text, offering a more satisfying- and
surprisingly robust- alternative to the stereotypical frustrating maze of IVR
options. Most importantly, thanks to advancements in API technology many of
these solutions can be implemented in a matter of weeks rather than months,
generating immediate results.

AI can also be used in optimizing financial
institutions’ marketing campaigns. When AI-based predictive modeling is
included in Digital Experience Platforms, financial institutions can run many
more-and meaningfully more effective- marketing campaigns without increasing
staff. Legacy rules-based programs, the likes of which have been used by banks
and credit unions for decades, require detailed oversight of each individual
campaign.  A team member must create the
campaign, establish rules for the desired targeting of prospects, monitor the
results and if possible, course correct when appropriate. Unless a bank has a
data scientist on the payroll, designing a high-performing rules- based formula
is often the most onerous of these tasks.

Most financial institutions employ multiple
methods of targeting prospects. Often, they create rules to isolate specific
products and groups of consumers- for instance based on age, income, zip code
or recent life event (relocation, new baby, graduation, etc.) The variety of
data points available, from both internal and external sources, is virtually
limitless. Harmonizing these options can pose an insurmountable hurdle for
legacy models, however, given the need to convert them into an actionable
target list, and ultimately measure the actual results.

With predictive modeling, the AI engine determines
which characteristics are most correlated with the desired behavior- usually
propensity to buy a given product. The engine optimizes across the countless
permutations of inputs, monitoring results and adjusting formulas when needed. Thanks
to machine learning, future campaigns become more effective as well.  And the FI’s marketing team is freed to focus
on value-added tasks like messaging and outreach rather than the technical nuances
of demand generation.

These same capabilities can be used in
solutions that offer opportunities to have an even deeper engagement with the mobile
banking channel via an Instagram-like experience. Younger demographics- a key
bank and credit union growth segment- have demonstrated a clear preference for
interacting with this type of visual content, and with concise packets of
information.

Providing financial institutions with the
ability to tailor a “Spotlight Story” to each client, serving up an array of
customized offers, news, financial advice, etc., through the mobile banking app,
further strengthens the customer experience. Content can be hyper-localized
(promoting area blood drives or neighborhood businesses, for instance) and
constantly updated for immediate relevance and to further appeal to each
client’s demonstrated preferences.

Too often, AI is mistakenly equated with the
notion of robots taking away jobs from humans. AI powered solutions are most valuable
when they augment rather than replace existing staff. Marketing budgets are
already tightly stretched, and financial institutions have long been challenged
to generate demand across a variety of lending and investment products- as well
as deepen relationships. When deployed properly, AI can be the long-sought answer
to achieving these goals without adding to existing staff.

AI is revolutionizing business practices across
multiple areas of community financial institutions. This once esoteric technology
is delivering real results, enhancing the ability to respond to members in
real-time, speeding customer service, and opening avenues to marketing in a
respectful, personalized manner. Fintech companies have made the technology affordable for credit
unions and community banks and have greatly simplified and shortened the
implementation requirements. AI is no longer the exclusive domain of the megabanks- a
breakthrough that should benefit consumers, small business and community
institutions alike.

About Author
Jill Homan is president of DeepTarget, a FinTech company developing and deploying an open, data-driven customer engagement and cross-selling platform for credit unions and banks.

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