Fintech News

Fintech Revelations: 17 Surprising Customer Behavior Insights from Experts

By Brett Farmiloe

Posted on August 23, 2025

Fintech Revelations: 17 Surprising Customer Behavior Insights from Experts

Fintech experts have uncovered surprising patterns in customer behavior that challenge conventional wisdom. These revelations span from AI-driven analytics to the impact of seasonal trends on financial tool usage. Understanding these insights can revolutionize how businesses approach customer engagement, product development, and risk assessment in the financial technology sector.

AI-Driven Analytics Reveal Policy Misuse Trends
Digital Engagement Predicts Credit Repayment Success
Instant Bank Transfers Boost High-Value Purchases
Small Financial Habits Signal Loan Repayment Reliability
VIP Program Transforms Customer Spending Patterns
Faster Payouts Trump Higher Cashback Rates
Seasonal Spikes Drive Tax Credit Tool Usage
Hidden 17-Day Buying Cycle Boosts Conversions
After-Hours Searches Indicate Serious Franchise Inquiries
Currency Fluctuations Influence Payment Timing Strategies
Repeat Customers Drive Profit in Unexpected Months
Payment Delays Signal Impending Client Disengagement
Financial Framing Motivates Cybersecurity Action
Payment Authorization Speed Predicts Subscription Retention
Payday Influences Pet Care Service Bookings
Payment Friction Forecasts Customer Support Needs
Checkout Experience Predicts Customer Success Outcomes

AI-Driven Analytics Reveal Policy Misuse Trends

In one project, we supported a client building an expense management platform that used AI to categorize transactions and surface insights about user spending habits. By integrating predictive analytics into their dashboard, the platform was able to flag unusual behavior patterns and recommend actions, such as adjusting spending limits or changing reimbursement policies, before issues arose.

What surprised both us and the client was how small behavioral shifts, like a user regularly submitting expenses just under an approval threshold, could reveal broader trends in policy misuse or workflow inefficiencies. This wasn’t something that stood out in traditional reporting, but the AI-driven analysis made it clear. That insight led the client to rework their approval logic and improve policy communication, ultimately reducing friction between employees and finance teams. It showed how smart fintech tools can turn routine data into proactive decision-making.

Sergiy Fitsak, Managing Director, Fintech Expert, Softjourn

Digital Engagement Predicts Credit Repayment Success

I worked on a fintech analytics solution for a credit card collections team. The platform combined transaction history, repayment patterns, call center interactions, and external credit bureau data into a single behavioral model. It wasn’t just flagging who was late; it was predicting how likely and how soon each customer would self-cure, respond to a payment plan, or require escalation.

One surprising insight was that the strongest early predictor of repayment wasn’t income or credit score. It was the customer’s digital engagement in the week after missing their first payment.

Customers who logged into the mobile app and checked their statements within 5-7 days after delinquency had a 40% higher chance of curing without further intervention. In contrast, customers who ignored all digital channels but answered the first collections call were still more likely to default than those who engaged digitally but didn’t pick up calls.

This shifted the strategy: instead of immediately calling everyone at day one or two, the team prioritized outreach to digitally disengaged customers, while giving engaged customers more time and softer nudges (push notifications, in-app reminders) before escalation. The result was fewer unnecessary calls, reduced operational costs, and a measurable increase in cure rates simply by using engagement signals as an early triage filter.

SAI KIRAN NANDIPATI, Solution Architect, EY

Instant Bank Transfers Boost High-Value Purchases

We integrated a fintech analytics tool last year that pulled together transaction histories, purchase frequency, and payment preferences into a single dashboard. One feature used AI to flag “likely to purchase” segments based on subtle patterns — like day-of-week spending spikes or average cart value trends. The surprising insight? A large chunk of our high-value customers weren’t using credit cards at all — they preferred instant bank transfers, especially for repeat purchases. That changed our promo strategy: instead of pushing card-linked offers, we introduced instant-transfer discounts and saw a 12% lift in repeat orders within a month.

Dhaval Alagiya, Digital Marketing Executive, Brainvire

Small Financial Habits Signal Loan Repayment Reliability

A few years ago, we implemented an AI-driven analytics platform that aggregated customer transaction data, credit usage patterns, and engagement history into a single, dynamic profile. Initially, our goal was simply to refine risk scoring for microloans. However, as we worked with the system, it began revealing patterns far beyond creditworthiness.

One of the most surprising insights was how strongly non-financial behaviors predicted repayment reliability. For example, customers who consistently topped up their mobile phone balances in small, regular amounts were far more likely to make timely loan repayments than those with erratic top-up patterns, even when income levels were similar. This small, seemingly unrelated habit indicated discipline and predictable cash flow management.

That discovery changed how we approached product design. We began using these behavioral indicators not only for lending decisions but also for offering tailored savings products and financial education prompts at exactly the right time. It improved repayment rates and deepened customer trust because offers felt relevant, not generic.

In short, fintech gave us the ability to listen to customer behavior at scale — and sometimes, the quietest signals spoke the loudest about future actions.

Andrew Izrailo, Senior Corporate and Fiduciary Manager, Astra Trust

VIP Program Transforms Customer Spending Patterns

When we implemented advanced analytics tools to calculate Customer Lifetime Value across our e-commerce operations, we uncovered a pattern that genuinely surprised our entire executive team. The data clearly showed that just 20% of our customer base was responsible for generating 80% of our total sales volume, which completely transformed our approach to customer segmentation.

This insight prompted us to develop a tiered VIP loyalty program specifically targeting these high-value customers, resulting in a remarkable 2.5x increase in spending among program participants. The success of this data-driven initiative reinforced our belief that understanding customer behavior patterns is fundamental to sustainable growth in today’s competitive marketplace. Looking back, I’m convinced that investing in robust analytics capabilities was one of the most consequential decisions we made for our business.

Ahmed Yousuf, Financial Author & SEO Expert Manager, CoinTime

Faster Payouts Trump Higher Cashback Rates

One fintech tool that really changed how we think was Plaid. With user permission, we started analyzing spending behavior — specifically, when and why people used (or skipped) cashback portals. I expected brand loyalty to be the main driver, but it wasn’t — speed won.

What surprised me most? Users weren’t loyal to a single cashback site. They’d bounce between platforms based on who paid out faster — even if the rate was slightly lower. That totally flipped how we prioritize recommendations. We started surfacing not just the “highest” cashback — but the fastest-paying ones too.

It was a good reminder: logic doesn’t always beat psychology. A faster $5 beats a slower $6 in most people’s minds.

Ben Rose, Founder & CEO, CashbackHQ.com

Seasonal Spikes Drive Tax Credit Tool Usage

We built a platform designed to make tax credits and budgeting more accessible. As part of our development process, we started integrating a fintech analytics solution that could track how users interacted with different parts of our platform. We weren’t just collecting raw numbers — we were watching patterns unfold. We could see which features were used most, which ones were opened and abandoned, and even what time of year people needed the most help.

One insight that really surprised me was how sharply behavior shifted based on season and circumstance. For example, we assumed most people would explore tax credit tools steadily throughout the year, especially if they had small businesses or were self-employed. But the data showed big spikes in engagement right before quarterly tax deadlines and again during periods of financial uncertainty, like a market dip or major policy change. These weren’t slow ramps — they were sharp, concentrated bursts of activity.

What this told us was that our users weren’t just passively managing their money — they were reacting to stress, deadlines, and economic signals. Once we understood that, we were able to do something valuable: meet them there. We started sending targeted nudges and recommendations just before those key moments, which improved engagement and helped users make smarter choices without feeling overwhelmed.

From a CPA’s perspective, this kind of insight is gold. I’ve worked with clients one-on-one for years, but even the best financial conversations don’t always reveal behavior like this. Fintech tools can look at thousands of users at once, analyze how they move through decisions, and spot patterns that help us serve everyone better. It moves financial guidance from being reactive to being proactive.

What I learned is that the more specific your data, the more personal your advice can become. It’s no longer about building a one-size-fits-all experience. It’s about using what you know — down to the week or even the day — to deliver support that actually matters in the moment.

Fintech won’t replace personal financial planning, but it absolutely makes it smarter. It reduces the guesswork, brings new clarity to customer needs, and strengthens the relationship between advisor and client. For me, it’s one of the most practical and powerful changes I’ve seen in this field, and it’s only getting better.

Kevin Marshall, CPA, Smithii Tools

Hidden 17-Day Buying Cycle Boosts Conversions

I used a fintech tool called Mixpanel that tracked not just what my customers bought, but the exact sequence and timing of their purchases over months. At first, I assumed my biggest sales periods were tied to seasons or promotions, but the data revealed something different: my customers had very specific personal buying cycles that didn’t always match my marketing calendar.

What surprised me most was discovering that a large group of repeat buyers consistently placed follow-up orders about 17 days after their first purchase. This wasn’t obvious from looking at standard sales reports, but Mixpanel’s behavioral flow analysis made the pattern impossible to miss. Once I saw it, I shifted my outreach to land right before that 17-day window.

That one simple change led to a clear boost in conversions without increasing my ad spend. It showed me that customer behavior isn’t just about trends; it’s about hidden timing patterns you can act on.

Liam Derbyshire, CEO / Founder, Influize

After-Hours Searches Indicate Serious Franchise Inquiries

We integrated a fintech analytics tool to gain a deeper understanding of user interactions on our platform. This integration enabled us to track various customer behaviors, such as the frequency of franchise searches, engagement with specific franchise categories, and the time spent on our platform.

One surprising insight was the impact of timing on user engagement. We discovered that users who initiated their franchise searches after hours or on weekends, outside of their day jobs, were far more likely to proceed with inquiries or applications. This pattern wasn’t immediately apparent through traditional metrics.

With this knowledge, we adjusted our communication strategies to align with these behavioral patterns. We customized our outreach efforts, ensuring that users received timely information and support during these peak engagement periods. This approach led to a noticeable increase in engagement and a higher conversion rate from inquiries to applications.

This experience underscored the value of behavioral analytics in understanding and predicting customer behavior. By leveraging data-driven insights, we can enhance user experience and improve outcomes for both our users and our platform.

Alex Smereczniak, Co-Founder & CEO, Franzy

Currency Fluctuations Influence Payment Timing Strategies

Integrating a fintech analytics platform into our export invoicing and payment system was one of the most eye-opening aspects we encountered. This solution enabled us to trace not only payment schedules but also customer buying behavior patterns associated with currency fluctuations, seasonal purchasing trends, and even political shifts in individual regions. The most impressive finding? It turned out that some clients regularly delayed payments in off-peak quarters, not because they had cash flow problems, but because they were waiting to obtain more favorable exchange rates. This insight enabled us to reorganize our payment terms and achieve much more accurate forecasting.

Gary Winstanley, Managing Director, Leverbrook Export Limited

Repeat Customers Drive Profit in Unexpected Months

A while back, I started using a fintech tool that pulled together client transaction data, invoices, and payment histories in real time. One small business I worked with was convinced that December was their most profitable month because sales were high. But when the tool crunched the numbers, it turned out their real profits came in April and May — the so-called “slow months.” The difference was that in April and May, repeat customers were placing bigger orders with better margins, while December’s sales were high volume but low profit because of discounts. That finding totally changed the way they approached their marketing and managed their cash flow — and I’ve got to admit, it caught me off guard too.

Nitin Lilani, Tax Accountant, KPG Taxation

Payment Delays Signal Impending Client Disengagement

Yes, one standout experience was when we integrated a fintech analytics solution called Mixpanel (paired with Stripe data) to better understand client payment behavior and engagement cycles for our QA services.

Before that, we had a general sense of churn risk based on contract renewals, but no granular visibility into why some clients renewed enthusiastically while others went quiet after a few months. Once we linked our invoicing and usage data, we could track not just when clients paid, but how their payment patterns correlated with service utilization, ticket submissions, and release cycles.

The surprising insight was this: clients who delayed invoice payments by more than seven days weren’t just “bad payers” — they were often disengaging operationally, weeks before officially churning. They’d reduce test suite runs, cancel or skip review calls, and slowly disconnect from collaborative tools. Payment behavior turned out to be a leading indicator of product disengagement.

This completely shifted our retention playbook. Instead of chasing late payments reactively, we now treat payment lag as a customer health flag triggering proactive check-ins, workflow audits, and in some cases, offering automation boosts to re-engage before the relationship slips.

It taught me that in fintech data, the numbers are never just financial — they’re behavioral breadcrumbs.

Shishir Dubey, Founder & CEO, Chrome QA Lab

Financial Framing Motivates Cybersecurity Action

Integrating real-time network security alerts with financial data in our Cyber-Risk Financial Quantification service has transformed how business leaders view cyber threats. A surprising insight is that when vulnerabilities are expressed as potential monetary losses rather than technical jargon, decision-makers become much more engaged in addressing them. For instance, showing the impact of ransomware on operational costs and potential fines provides a visceral understanding that can motivate action.

This approach shifts the conversation from abstract risks to tangible financial consequences, leading to quicker approvals for security upgrades and more robust defense mechanisms. Framing these risks in terms of their effect on the bottom line resonates deeply, creating a sense of urgency that would often be absent in traditional risk assessments.

Matthew Franzyshen, Business Development Manager, Ascendant Technologies, Inc.

Payment Authorization Speed Predicts Subscription Retention

Our team saw great results using predictive analytics in our payment processing. By checking how people paid, we spotted when and how free trial users switched to paid plans.

What we learned was unexpected: when people tried to pay was a sign of whether they might cancel later. For example, those who took over 48 hours to authorize their first payment were 30% more likely to cancel in a month. This delay indicated they were less likely to stick around.

Knowing this, we improved our onboarding process. We sent reminders, provided payment options, and gave clear instructions to those who seemed unsure. Because of this, we saw a real increase in people going from trial to long-term subscriptions.

Hiren Shah, Owner, Anstrex

Payday Influences Pet Care Service Bookings

We integrated a fintech solution that allowed us to analyze recurring payments and booking trends across our customer base. This helped us predict peak periods for pet-sitting services and better allocate our team. One surprising insight was that many customers booked more frequently right after payday, which showed us how financial cycles directly influence pet care decisions.

Skandashree Bali, CEO & Co-Founder, Pawland

Payment Friction Forecasts Customer Support Needs

We integrated Stripe (Payment Element + Radar + Sigma) into our data warehouse and connected it with our CRM. Instead of treating payments as a back-office task, we used them as an early indicator of behavior. Sigma provided us with per-transaction details (payment method, 3-D Secure challenges, retries, time-to-success) that we mapped against downstream actions like course completion, refunds, and second purchases.

The surprise was how predictive “payment friction” was. Customers who encountered a 3-D Secure challenge or needed more than one attempt were about twice as likely to request a refund and far less likely to finish the first module — regardless of content topic or price. Conversely, Apple Pay/Google Pay checkouts had significantly lower refund rates and higher second-purchase probabilities.

In short, the way someone pays tells you a lot about the support they’ll need the following week.

We changed two things:

First, anyone with a high-friction checkout automatically received a personal onboarding email with a calendar link and an alternate payment option for future renewals.

Second, we added a day-5 “what to expect on your statement/quick-win checklist,” timed to when friendly-fraud chargebacks tend to spike.

The result was fewer disputes, faster time-to-first win, and an increase in cohort completion — without altering the curriculum.

So, here’s my advice for founders:

Direct your fintech data at outcomes you already care about (completion, refunds, repeat purchases) and treat the checkout as the beginning of customer success, not the end of marketing.

Justin Brown, Co-creator, The Vessel

Checkout Experience Predicts Customer Success Outcomes

I once used a fintech tool to analyze the spending habits of our customers in real-time. The tool proved to be really helpful as it didn’t just show transactions, but also grouped them into categories like travel, dining, and health.

The tool helped me realize that even small recurring charges, like multiple streaming services, could cause a significant shift in customer preferences, even faster than big one-time purchases.

Overall, the tool helped me predict when exactly users might downgrade or cancel subscriptions. This could not have been done with just traditional data views.

J. Ryan Smolarz, M.D., M.B.A., Founder | Commercial Real Estate Investor, VIENT