For years, fintech had a simple job: make money move faster.

Make payments easier.
Make banking more accessible.
Make lending less painful.
Make investing less intimidating.
Make insurance and credit feel less stuck in the past.

That was a huge shift. Fintech unbundled the old financial stack and made the front door of finance much easier to open.

But AI is pushing fintech into a deeper phase.

The next wave is not just about making financial activity faster. It is about helping financial institutions decide who and what to trust.

That is a much harder problem.

Because a payment company is no longer only moving money. It is judging whether a transaction is legitimate. A lender is no longer only collecting documents. It is deciding whether an applicant’s income, behavior, and risk profile can be trusted. An insurer is no longer only pricing policies. It is checking whether a claim, image, or document is real. A bank is no longer only monitoring accounts. It is looking for fraud, account takeover, money laundering, mule networks, and signs of customer distress.

This is the quiet shift happening underneath fintech:

From moving money to judging trust. And AI is becoming the engine behind that shift.

The reason this matters now is that trust is becoming harder to protect. Fraudsters are using AI too. Synthetic identities, deepfake onboarding, voice cloning, fake documents, phishing, and AI-generated scam scripts are all making financial deception faster and more convincing.

So the next fintech moat will not come from having the slickest app, the fastest onboarding flow, or the most impressive model demo.

It will come from making better trust decisions under pressure.

Today, we’ll explore:

— Naseema Perveen

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The data points toward a clear direction: AI is moving deeper into financial infrastructure, and fraud pressure is making trust automation more urgent.

Source

First, AI investment in financial services is scaling quickly. The World Economic Forum reported $35 billion in AI spending by financial services firms in 2023, with projected investment reaching $97 billion by 2027 across banking, insurance, capital markets, and payments. (World Economic Forum)

Second, AI use cases are shifting into core risk functions. The U.S. Treasury stated in March 2026 that AI is increasingly embedded in fraud detection, cybersecurity, credit underwriting, and operational risk management. (U.S. Department of the Treasury)

Third, supervisory bodies are paying attention because adoption is no longer theoretical. A 2025 World Bank survey on AI in supervision reported that among authorities with at least early AI adoption in their jurisdictions, common financial institution use cases included customer service chatbots and virtual assistants at 64 percent, fraud detection at 56 percent, and anti-money laundering use cases as another major category. (World Bank)

Lastly, fraud losses remain large even when some categories improve. A 2026 report summarizing recent identity fraud research said combined fraud and scam losses totaled $38 billion in 2025, down $9 billion from 2024, while 36 million people were affected. It also reported that traditional identity fraud losses remained at $27.3 billion in 2025. (Biometric Update)

The outlook is not simply “AI will make finance more efficient.”

The stronger forecast is this:

AI will become the decision infrastructure for financial trust. But as AI-generated fraud grows, trust systems will need to become continuous, explainable, collaborative, and resilient.

That creates a major opening for fintech companies building tools in identity verification, fraud intelligence, underwriting infrastructure, compliance automation, model governance, explainability, data lineage, synthetic identity detection, scam prevention, and secure AI operations.

The biggest prize may not go to the flashiest consumer app.

It may go to the companies that become the invisible trust rails behind finance.

Underwriting is one of the most important battlegrounds.

Traditional underwriting often relies on static snapshots: credit scores, income documentation, employment history, collateral, prior claims, or historical repayment behavior.

AI enables a more dynamic model. It can analyze cash flow, transaction histories, document quality, behavioral patterns, market conditions, business performance, fraud signals, and alternative data sources. It can help lenders and insurers separate thin-file but trustworthy applicants from risky applicants who look good on traditional metrics.

That matters because financial inclusion and risk management often sit in tension.

Approve too narrowly, and you exclude good customers. Approve too broadly, and losses rise. Automate too aggressively, and you risk unfair or unexplainable decisions. Stay too manual, and competitors move faster with lower costs.

The opportunity is not simply automated underwriting.

It is trustworthy underwriting.

That means three things.

First, better signal quality. AI should help institutions see real repayment capacity or risk more clearly, not just add more variables.

Second, better decision governance. Models need monitoring, performance testing, bias checks, drift detection, and human oversight for edge cases.

Third, better customer experience. Applicants should not feel judged by a machine they cannot understand. Even when the full model is complex, the process should provide clarity, next steps, and appeal paths where appropriate.

In the next fintech cycle, underwriting infrastructure will become more modular. Lenders will want tools that verify income, detect document fraud, classify cash flow, explain risk, monitor portfolio drift, and satisfy compliance teams. Insurers will want similar capabilities for claims, policy pricing, and risk selection.

The winners will make underwriting faster without making it feel reckless.

Fraud is becoming more automated, more personalized, and more synthetic.

AI can generate convincing fake documents. It can write better phishing messages. It can clone voices. It can create fake customer support interactions. It can help fraudsters test systems faster. It can make scams feel human.

The defensive side needs AI because manual fraud review cannot keep up.

But fraud detection is also moving from isolated alerts to network intelligence.

The future is not one model asking, “Is this transaction suspicious?”

It is a system asking:

Is this identity real?

Has this document appeared elsewhere?

Does this device connect to other risky accounts?

Does this behavior match the customer’s history?

Is this payment part of a mule network?

Is the customer being manipulated by a scammer?

Is the account newly opened and rapidly changing behavior?

Does the receiving account show patterns seen in known scams?

Can we intervene without humiliating or blocking the customer unnecessarily?

This is why fraud prevention will become more collaborative. Financial institutions, payment networks, identity providers, telecoms, marketplaces, and regulators will need better ways to share signals safely.

The European Payments Council’s 2025 Payments Threats and Fraud Trends Report noted that European policy proposals include provisions on data sharing for fraud prevention, and it also pointed to a SEPA-wide platform for sharing fraud information between payment scheme participants. (European Payments Council)

Fraud is a network problem. Trust must become a network defense.

There is a darker version of this future.

In that version, finance becomes faster but colder. Every customer is scored, watched, filtered, and nudged by invisible systems. Legitimate users get blocked without explanation. Vulnerable customers are mislabeled as risky. Bias hides inside data pipelines. Human teams defer too much to model outputs. Vendors become concentrated points of failure. Regulators discover problems after damage has already scaled.

This is the danger of automating distrust.

Finance cannot simply become a suspicion machine.

The goal should not be to maximize blocking. It should be to maximize justified confidence.

That distinction matters.

A good trust system helps more legitimate customers get approved, protected, and served. It reduces unnecessary friction. It catches fraud earlier. It gives humans better evidence. It explains enough to create accountability. It improves over time.

A bad trust system hides behind complexity. It rejects edge cases. It treats all anomalies as threats. It pushes responsibility onto users. It creates compliance theater instead of real governance.

This is why trust automation needs principles.

To understand where the opportunity sits, think of modern fintech trust as a five-layer stack.

This layer answers: Is this person, business, device, or account real?

AI is being used to detect forged IDs, synthetic identities, deepfakes, document manipulation, account takeover signals, and suspicious onboarding patterns. The challenge is no longer simply checking whether a document looks valid. It is understanding whether a whole identity pattern makes sense.

Identity trust is becoming continuous. A customer may be legitimate at onboarding but compromised later. A device may be safe today and risky tomorrow. A business may appear normal until transaction patterns change.

The future of identity in fintech will be dynamic, not static.

This layer answers: Can we trust the information being submitted or analyzed?

Financial decisions depend on data: income statements, bank feeds, tax records, invoices, payroll history, cash flow, transaction data, credit files, insurance documents, and customer declarations.

AI can help verify, classify, extract, and cross-check this data. It can spot anomalies that humans miss, such as reused documents, inconsistent formatting, manipulated statements, or mismatches between declared and observed behavior.

But data trust also includes consent, provenance, lineage, privacy, and compliance. A model trained on questionable data can become a liability. A 2026 legal analysis of financial services AI risk highlighted “model destruction” risk, where regulators may order algorithmic disgorgement if models are trained on improperly sourced data. For firms relying on AI-driven underwriting or fraud detection, losing a core model could become a serious operational disruption. (Lowenstein Sandler LLP)

Data is not just fuel. It is evidence.

This layer answers: Does this activity fit what we know?

Fraud detection increasingly depends on behavior. How does the user type? How quickly do they move through the flow? Is the transaction consistent with their history? Is the merchant pattern normal? Does the login location make sense? Is the account suddenly behaving like a mule account? Is there a network of related accounts showing coordinated behavior?

AI is powerful here because behavior is high-dimensional. A human reviewer may see five signals. A model can evaluate thousands.

But behavioral trust must be handled carefully. Customers should not be punished for being unusual. Models must distinguish between legitimate life changes and risky anomalies. A person moving cities, changing jobs, traveling, or receiving a large payment should not automatically become suspicious.

Good AI trust systems reduce friction for legitimate users while escalating genuine risk.

Bad ones turn finance into a maze of invisible suspicion.

This layer answers: Can we trust the outcome?

This is where underwriting, fraud scoring, claims approval, credit limits, transaction approvals, and compliance alerts live.

A decision is trustworthy when it is accurate, explainable enough for its context, monitored over time, tested for bias, and connected to human escalation when needed.

For underwriting, this is especially important. AI can expand access to credit by incorporating richer data and improving risk segmentation. But it can also reproduce or amplify bias if the data and design are weak. That is why model monitoring, bias reviews, and lifecycle governance matter. A 2025 Orrick report on AI models in financial services emphasized regular monitoring and bias reviews for models used in areas such as credit scoring, fraud detection, and underwriting. (Orrick Media)

The fintech winners will not be the companies that say “our AI made the decision.”

They will be the companies that can say “we know why the decision was made, how it performs, where it fails, and how we correct it.”

This layer answers: Can customers, partners, regulators, and markets trust the organization using AI?

This is the highest layer, and it may become the most valuable.

A company’s AI system can be technically impressive but institutionally weak. It may lack governance, audit trails, incident response, compliance alignment, vendor oversight, security controls, or customer transparency.

Institutional trust turns AI capability into market permission.

Banks and insurers will not partner deeply with AI fintech vendors unless they trust their controls. Regulators will not tolerate black-box decisioning in sensitive financial contexts without oversight. Customers will not stay with firms that make high-impact automated decisions feel arbitrary.

In this era, the best fintech companies will not only sell software.

They will sell confidence.

For this week’s feature, we asked Vicky Emerson, Founder of We Love Data

“How AI is impacting industries right now, including fintech?”

Here’s how she puts it:

Vicky Emerson is the Founder of We Love Data and AI, AI education specialist, and MSc Artificial Intelligence student at the University of Hull. With a background in teaching spanning 20 years, she is passionate about making AI simple, practical, and accessible, helping people build confidence and real-world skills in a rapidly changing digital world.

As AI becomes more embedded in underwriting, fraud detection, and compliance, what is the biggest trust gap financial institutions still need to solve: data quality, explainability, governance, customer protection, or regulatory confidence?

We’d love to hear your perspective.

Email your thoughts to: [email protected]
Selected responses will be featured in next week’s edition.

Most fintech teams track conversion, approval speed, fraud rate, default rate, chargebacks, customer acquisition cost, and retention.

They should also track trust metrics.

Examples include false positive rates, appeal outcomes, manual review quality, model drift, customer friction points, explainability coverage, verified data provenance, scam intervention success, and regulator-ready audit completeness.

What gets measured gets improved.

AI should not remove humans from every financial decision. It should make human judgment more focused.

For low-risk, high-volume decisions, automation can work well. For high-impact or ambiguous cases, humans should remain part of the loop.

The strongest systems will use AI to prepare evidence, summarize risk, and recommend actions, while still allowing human teams to override, investigate, and improve the process.

Explainability is not a feature to bolt on later.

Underwriting, fraud detection, and compliance models need decision logs, reason codes, evidence trails, and review workflows. Teams should know which signals influenced outcomes and whether those signals are stable, fair, and legally usable.

The more sensitive the decision, the more important the explanation.

Models are not static software. They drift.

Fraud patterns change. Customer behavior changes. Macroeconomic conditions change. Data quality changes. Attackers adapt.

Fintech teams should monitor model performance continuously, with thresholds for retraining, rollback, escalation, and independent review.

A model that performed well six months ago may be dangerous today.

Financial firms need to know where their models come from, what data trained them, which vendors support them, what happens if access is lost, and how sensitive information is protected.

Vendor concentration, data leakage, model misuse, and unclear intellectual property rights can all become trust failures.

The AI supply chain is now part of financial risk management.

Fraud tools often focus on protecting the institution. The next generation will also protect the customer.

That means scam warnings, payment friction when manipulation is likely, identity protection, personalized alerts, and clearer education at the point of risk.

Trust is not only about stopping bad actors. It is about helping good customers feel safe.

In weak organizations, compliance is treated as a blocker.

In strong AI fintech organizations, compliance becomes a design partner.

The earlier compliance, legal, security, and risk teams are involved, the faster the company can scale safely. Governance is not the opposite of innovation. In regulated finance, governance is what makes innovation deployable.

1. Verify
Confirm that the person, business, device, document, and data source are legitimate.

2. Interpret
Use AI to understand behavior, context, patterns, and anomalies across financial activity.

3. Decide
Apply AI-assisted scoring, underwriting, fraud detection, and risk classification with governance.

4. Explain
Maintain decision trails, reason codes, model documentation, and customer-facing clarity.

5. Monitor
Track drift, bias, performance, fraud adaptation, false positives, and operational failures.

6. Protect
Use AI not only to protect the institution, but also to protect customers from scams, identity theft, and manipulation.

Core idea:
The strongest fintech companies will not automate finance blindly. They will automate justified confidence.

Build around a trust pain point, not just an AI feature. The best opportunities are where manual trust decisions are slow, expensive, inconsistent, or under attack.

Strong wedge examples:

Fraud review automation
Synthetic identity detection
AI underwriting explainability
Document verification for lending
Scam intervention tools
AML investigation copilots
Insurance claims verification
Model monitoring for regulated finance
Consent and data provenance infrastructure

Start with use cases that have clear ROI and manageable risk. Fraud detection, document review, compliance investigation support, and customer support summarization are often easier entry points than fully automated high-impact underwriting.

Build an AI governance layer before scaling decision automation.

Look for companies that own proprietary trust signals, not just model wrappers. Defensibility will come from data networks, workflow integration, regulatory credibility, performance history, and distribution into regulated institutions.

Make risk, compliance, security, product, and data science work together from the beginning. In AI finance, the product is only as strong as the governance around it.

The first fintech era digitized access.

It gave people easier ways to open accounts, send money, invest, borrow, and interact with financial services without walking into a branch or filling out endless paperwork.

The second fintech era optimized speed.

It made onboarding faster, payments instant, credit approvals smoother, trading cheaper, and customer experiences more seamless. For a long time, speed felt like the main competitive advantage.

But the next fintech era will be different.

It will be about automating trust.

That does not mean replacing trust with algorithms. It means building systems that can verify identity, interpret behavior, detect risk, explain decisions, protect customers, and adapt as threats evolve.

Because finance has always been a trust business.

Every loan is a trust decision.
Every payment is a trust decision.
Every insurance claim is a trust decision.
Every compliance alert is a trust decision.
Every account opening is a trust decision.

AI makes it possible to make those decisions faster and with more context. But speed alone is not enough. In finance, a faster wrong decision is not progress. It is risk at scale.

That is why the next fintech winners will not simply be the companies that automate the most. They will be the companies that automate with accountability.

They will build systems that can show why a decision was made. They will know when a model is drifting. They will design human escalation into high-impact moments. They will protect customers from scams, not just protect themselves from losses. They will treat compliance, security, and explainability as part of the product, not as paperwork added later.

This is where the real moat forms.

Not in the model alone.
Not in the interface alone.
Not in the data alone.

But in the ability to make trustworthy decisions under pressure, again and again.

AI will become finance’s trust engine.

But the companies that win will be the ones that prove the engine can be trusted.

—Naseema

Writer & Editor, The AIJ Newsletter

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