Key Takeaways

• Identify hidden flaws in your company’s performance metrics by engaging BI consultants who audit beyond surface-level dashboards.
• Observe how BI consultants map data sources, fix broken logic, and eliminate inconsistent reporting to ensure data accuracy.
• Appreciate the consultant’s role in providing context and bridging communication gaps between finance, product, and compliance teams.
• Understand that BI consultants in 2025 focus more on strategic problem-solving within specific fintech areas than just building reports.

This article is based on the Business Intelligence (BI) implementation portfolio of Belitsoft, a custom software development firm with 20+ years of expertise in Fintech. This company confirmed its reputation with a 4,9/5 score from clients on such authoritative review platforms as Gartner, Goodfirms, and G2. Customers have collaborated with Belitsoft for 5 years on average. Their BI consultants and dedicated Power BI developers design bespoke analytics solutions, from Power BI dashboards to full-scale data systems, assisting projects with their deep technical and domain know-how.

Role Overview

Most fintechs are already on Snowflake, BigQuery, or Redshift. They’ve licensed Looker, Tableau, or Power BI. They’ve piped data through Fivetran and dbt. What’s missing is someone who can figure out why the dashboards aren’t showing what the team expects.

Most work with product or operations teams and do the work frontline analysts don’t have time for: map data sources, flag gaps, rebuild logic, and eliminate duplicate queries that are out of sync. In a payments startup, they may clean up customer conversion logic across six tools. In a neobank, it’s about separating fraud screening metrics from risk signals used in credit modeling.

BI consultants in 2025 may own reporting end-to-end or hand off clean datasets and logic specifications to in-house analysts who pick up from there.

The strongest consultants act like internal auditors with storytelling abilities. They walk into a fintech firm, audit the performance metrics that leaders already think are “fine”, and quietly surface the flaws.  

That kind of intervention is why senior BI consultants often become the tie-breaker between finance and product, or between growth and compliance. They know how the data got built. That context leadership teams can’t get from dashboards alone.

The role still includes hands-on work: writing queries, tuning models, managing BI tools. But the value lies in which problems they choose to solve. Different fintech segments rely on data, but not in the same way. A BI consultant working in payments has little in common with one inside an insurtech firm, except the job title.  

Fintech Segment Specializations

Neobanks

BI consultants in digital banks obsess over micro-metrics: form abandonments, failed verifications, fraud-attempt signals hiding inside login spikes. The questions are daily: Why are users dropping on step two? Which device types have higher rejection rates? 

When sign-ups tank in one geography, it’s BI that flags the anomaly. In a neobank, BI is the early warning system.

Lenders

Online lenders have a narrow margin for error. Push growth too hard, defaults spike. Get conservative, you stall. BI consultants sit between risk and revenue – modeling who qualifies, at what price, and how tight the funnel can get before conversion collapses. BI consultants may realize that nudging APR down by just 0.3% for a low-risk cohort may produce a 15% bump in approval rates – without raising portfolio risk. Those are revenue levers. And they only work when someone’s watching the full funnel – from ad click to final repayment – and tuning it constantly.

Insurtech

Insurance startups live or die by the spread between premium and payout. BI synthesises behavior, geography, weather patterns, telematics, and claims history into inputs for pricing and fraud detection. When rain volume jumps in a zip code and claims spike two days later, BI spots the correlation. When one adjuster starts filing unusually fast approvals – again, BI flags it. This is risk segmentation on a rolling basis.  

Payments

Payments teams want uptime. If a card processor starts rejecting transactions at 1 a.m., BI has to know before support tickets come in. Transaction failure rates, retry patterns, latency across gateways – all of it monitored in real time, broken down by region, method, and partner. If approval rates for payments or transactions suddenly drop by 2% in Canada, someone needs to take action – either redirect the traffic to a more reliable provider or investigate the one that’s causing the problem. Stripe didn’t scale because they built good dashboards. 

Wealthtech

Robo-advisors and digital investment platforms need retention. But the churn here is silent. BI teams monitor engagement patterns to catch drift early: logins slowing, feature usage dropping, deposit activity freezing. Using green/yellow/red to track how engaged users are is a way to spot who’s active, who’s slipping, and who’s about to leave. One team used them to trigger re-engagement flows for red-tier users and cut churn by 12% without changing the product. BI in wealthtech is the memory – tracking what users do, when they disengage, and how to win them back.

Crypto

Fraud, hacks, or system failures can happen for crypto companies in real time, and they have to catch them in seconds. BI consultants here operate like command center leads. Thousands of trades per second, multiple chains, high-frequency bots, whales, scam wallets – it all flows in at once. When withdrawal rates spike at 3:07 a.m., someone needs to call fraud escalation by 3:08. Anomalies don’t announce themselves. BI systems do. Dashboards here are for crisis avoidance. Segmentation matters too: how retail users behave vs. institutional API traders, what drives trading surges, which pairs carry hidden risk. 

Required Skills for BI Consultants in 2025

Technical proficiency

By now, every senior BI consultant can query relational databases, pull from Snowflake or Redshift, and debug a broken dashboard with their eyes half-closed. SQL is the main language. Most know enough Python or R to automate the boring, repetitive stuff and clean it up, or run simple regressions when business users start asking predictive questions they don’t fully understand. The stack varies – Power BI, Tableau, Looker.

The standout skill is judgment

When the CAC trend line breaks, the business wants to know if the metric is wrong or the market has shifted. That’s not a visualization problem. And a good BI consultant figures it out without asking for a two-week sprint. The best consultants check the automation workflow, trace the joins, review the filters, and walk into the meeting with a fix – or a better hypothesis.

Domain fluency

Fintech teams don’t want BI to learn on the job. You’re expected to know what drives the revenue model. That means different things in different verticals. In lending: approval rates, default curves, pricing levers. In payments: authorization rates, interchange math, fraud losses. In wealthtech: AUM dynamics, user tiers, churn behavior. You can’t optimize what you don’t understand. And you can’t advise stakeholders if you don’t know what the numbers mean in context.

Consultants who lack this grounding end up chasing data artifacts. The ones who have it can step into a planning meeting and immediately spot which growth projections are built on unstable assumptions – or which retention metric hides the drop-off visible after slicing the data.

Communication is most of the job

Finding a pattern is only valuable if someone changes behavior because of it. So BI consultants need to explain what the numbers actually say, and what the business should do next. Not with a jargon-dense Notion doc. Just by walking a product manager or a growth lead through the metric and how it moved.

This includes knowing when to say “This retention dip is driven entirely by users acquired through the affiliate channel after day 7. Everyone else is flat”. That sentence does more than any dashboard ever will. And it’s the kind of sentence that only gets said by someone who knows the audience, and knows the data.

Certifications?

Degrees? Useful, sometimes. Certifications? Helpful, if they prove something you’ve already done. But nobody’s getting hired in 2025 because they passed a Power BI exam. 

What makes someone stand out is the pattern in their past work: Did they build a new metric that became the company’s North Star? Did they disprove an expensive product assumption using cohort logic? Did they refactor the reporting layer to eliminate contradictory revenue views?

Compliance 

In fintech, every insight exists inside a legal framework. If you don’t know how data privacy laws impact what can be tracked, or how auditability affects how dashboards are built, it’s’ the issue. The best BI consultants in 2025 know which data can be queried, which teams need to be notified before changes, and how to document their work in a way that survives scrutiny.

That’s the difference between a BI consultant and someone with a dashboard portfolio.

In 2025, the role of a BI consultant in fintech doesn’t resemble the job posting that got them hired. What used to be a data analyst with a dashboarding license is now a strategic operator wired into decisions across risk, product, growth, and compliance – often at once. The role has expanded because the questions got harder.

Industry Trends in Fintech BI (2025)

AI didn’t replace BI

The shift is from static reporting to predictive insight. Today’s BI isn’t just about showing what happened – it feeds into models that shape what happens next. 

However, if the wrong signal gets pulled into a credit model – say, mislabeled user income – it can skew risk scores and approvals. That one bad input can tank an entire cohort’s performance. And if BI doesn’t catch the trend, the team ends up solving the wrong problem.

BI teams sit inside projects from the start

Consultants now sit in product squads, growth teams, underwriting meetings. They help define experiments, not just report the results. They surface issues before NPS drops. They tell the CFO whether the revenue number will hold – before earnings week. This only works because the best BI consultants in 2025 speak both languages: business urgency and data precision.

Cross-functional is the job format. One week, a BI lead refactor acquisition metrics for marketing. Next, they validate a new fraud rule’s impact on approval rates. The toolset hasn’t changed much, but the rhythm has.

The compliance layer got sharper

With expanding state and national privacy regulations, BI teams work under constraint. Data access controls, audit readiness, and privacy-safe reporting are baked into how dashboards get scoped. One consultant’s dashboard now needs to mask personal identifiers, track consent, and survive a regulatory review. That’s part of the definition of “working”.

It shapes how data is used from the beginning. When building new customer segmentation, BI teams now ask first: What can we store? Who gets to see it? Will this logic pass the audit in 18 months? 

The job market reflects all of this

Demand outpaces supply. Strong fintechs aren’t waiting for perfect resumes. They’re hiring consultants who’ve already solved hard problems, not just ones who’ve mastered the software. Compensation has followed. Six-figure salaries are common. High-skill candidates – especially those fluent in real-time analytics, AI tools, or risk modeling – push above that quickly. And in early-stage firms, BI consultants often get equity because they’re the ones identifying the path to product-market fit in the first place.

BI consultants still exist. Titles like Analytics Partner, Growth Analyst, Insights Lead, Decision Science Manager are now more common – especially in product – and strategy-heavy organisations. All of these roles deal with the same core responsibility: making data useful for decision-making.

About the Author:

Dmitry Baraishuk is a partner and Chief Innovation Officer at a software development company Belitsoft (a Noventiq company). He has been leading a department specializing in custom software development for 20 years. The department has hundreds of successful projects in such services as healthcare and finance IT consulting, AI software development, application modernization, cloud migration, data analytics implementation, and more for US-based startups and enterprises.