Fintech growth is returning, but growth alone is no longer enough to separate market leaders from the rest. The next stage of fintech will be shaped by how well organizations understand the consumers they serve, how accurately they assess risk and how consistently they make decisions across the customer lifecycle. That requires more than speed, more data or a single new model. It requires a unified view of the consumer that brings together identity, credit and behavioral signals into one decisioning strategy.
Experian’s 2026 State of Fintech Report identifies partnerships, data and fraud as three forces shaping the next phase of fintech growth. The report also makes a clear point: institutions that integrate these forces into cohesive strategies will be better positioned to grow with confidence.
For many fintechs, the challenge is not a lack of innovation. It is the increasing complexity of turning innovation into scalable, explainable and profitable growth. Fintech organizations span a wide range of maturity, from early-stage startups to scaled lenders, and many are experimenting with new products, technologies and customer engagement models at the same time. That creates opportunity, but it also creates pressure to make more disciplined decisions. The market is rewarding institutions that connect product strategy, risk management and customer experience in a more coordinated way. This is why the unified consumer view is becoming so important. It helps fintechs turn fragmented signals into consistent decisions that support both growth and resilience.
Why a unified consumer view matters now
A unified consumer view means bringing together the signals that define a customer’s identity, credit behavior, financial capacity and risk profile. It moves fintechs away from isolated decision points and toward a more connected picture of the customer across origination, account management and servicing. This matters because consumer behavior is becoming more fluid, fraud is becoming more sophisticated and product strategies are becoming more specialized. A customer may appear strong through one lens and risky through another. An application may pass an onboarding check, but later show behavior that suggests emerging fraud or repayment stress. Without a connected view, those signals may stay trapped in different systems or teams.
The 2026 State of Fintech Report highlights this shift across several areas. Fintechs are managing credit cards and unsecured personal loans with greater precision, recognizing that each product requires different strategies and risk controls. Credit cards require ongoing account management because exposure continues after origination. Unsecured personal loans follow a fixed repayment structure, which makes underwriting precision especially important at the point of origination. These differences show why a one-size-fits-all strategy cannot support modern fintech growth. A unified consumer view helps lenders apply the right data, risk framework and customer strategy to the right product at the right time.
Siloed decisions create blind spots
Many fintechs already use multiple sources of data. They may rely on traditional credit data, alternative data, fraud tools, cash flow information, identity verification and internal account performance data. If those signals are managed separately, the organization may still lack a clear view of the customer. Data can become fragmented. Risk teams can reach different conclusions than fraud teams. Product teams can pursue growth without a full understanding of emerging portfolio pressure.
The State of Fintech Report points out that fintech competition is increasingly defined by the ability to align data strategies with decision frameworks. That means data is not just a support function. It is becoming central to growth, risk management and customer experience. Organizations are investing in richer datasets and more advanced analytics, but the differentiator is how effectively those inputs are operationalized. This is where many fintechs still have work to do. The value comes not from any single dataset, but from how signals are layered, interpreted and applied together.
For example, a lender may understand a consumer’s credit score, but that does not always reveal broader financial behavior. Cash flow data may add insight into income and expenses, but it needs to be categorized and normalized to support reliable decisions. Identity signals may help detect fraud, but they become more powerful when combined with credit and behavioral data. A unified view brings these inputs together so fintechs can better determine whether a customer represents a growth opportunity, a fraud risk, an emerging credit risk or a borrower who needs a different product experience.
Product complexity requires better decisioning
The need for a unified consumer view becomes even clearer when looking at how fintechs manage different credit products. Fintech lenders continue to originate approximately 1.5 unsecured personal loans for every one credit card, which reinforces the importance of both products within portfolio strategy. Credit card originations continue to grow moderately while unsecured personal loan originations have slowed after tighter lending standards. These patterns suggest that fintechs are not simply shifting from one product to another. They are becoming more mature in how they manage each product based on its structure, risk profile and consumer use case.
Credit cards and installment loans behave differently. Credit cards introduce ongoing exposure and require active account management, line management and monitoring of utilization behavior. Unsecured personal loans carry fixed terms and structured repayment schedules, which makes origination quality especially important. For fintechs, this means product strategy and risk strategy must be tightly connected. The same consumer may need to be evaluated differently depending on the product, loan amount, repayment expectations and observed behavior. A unified consumer view gives lenders the context needed to make those differences actionable.
This is also where segmentation becomes more sophisticated. The State of Fintech Report’s loan segmentation framework connects strategy, risk and data advantage across small-dollar, mid-tier and large-ticket loans. Small-dollar lending can support thin-file acquisition, but may require alternative data and stronger identity visibility. Mid-tier lending may involve debt consolidation and cash flow pressure, where transaction insights and trended data can be particularly useful. Large-ticket lending can support higher-value growth, but it also creates greater exposure and may require a fuller combination of credit, fraud and identity signals. This kind of framework helps fintechs align product strategy with risk and data strategy in a more deliberate way.
Fraud is making the unified view even more urgent
Fraud is another reason fintechs need to move beyond siloed decisioning. Fraud is becoming more complex across the customer lifecycle. Synthetic identities, first-party misuse and AI-driven threats are reshaping the risk landscape. Traditional controls that focus primarily on onboarding are no longer enough. Effective strategies now require continuous monitoring across account access, transactions and servicing.
That shift changes how fintechs should think about customer intelligence. Fraud is no longer something that only happens at the point of application. It can emerge later through account behavior, suspicious activity or patterns that look normal when viewed in isolation. Advanced identity signals, including email intelligence, are becoming more central to fraud prevention because they add context that traditional data may not capture. The report also highlights Experian’s acquisition of AtData as part of a broader recognition that email-based identity signals represent a critical layer in digital identity and fraud detection.
The takeaway for fintech leaders is clear. Identity, fraud and credit risk cannot be treated as separate problems. A customer who appears creditworthy may still present identity risk. A fraud signal may also influence credit exposure. A repayment pattern may reflect financial stress, misuse or both. A unified view helps lenders evaluate these signals together so they can make decisions with more confidence and less friction for legitimate customers.
Trust is becoming a growth strategy
Trust has always mattered in financial services, but fintechs now need to think about trust as a measurable part of decisioning. Customers expect fast applications, seamless experiences and fair outcomes. Regulators and internal governance teams expect transparency, explainability and consistency. Business leaders expect growth without unnecessary exposure. These expectations are difficult to meet when data and decisions are fragmented.
The State of Fintech Report’s 2026 action playbook identifies trust as a function of decision accuracy, identity confidence and customer transparency. That framing is important because it moves the conversation beyond speed alone. A fast decision is not valuable if it approves the wrong customer, declines a good customer or creates unnecessary friction in the wrong place. Fintechs should evaluate where friction improves outcomes, such as preventing fraud or identifying risk, and where it creates unnecessary loss of good customers. For many lenders, the path forward is not removing friction everywhere. It is applying the right level of friction at the right moment based on a clearer view of the consumer.
This is where unified decisioning becomes a competitive advantage. It allows fintechs to create experiences that feel faster and more relevant while still protecting the portfolio. It supports better segmentation, more informed offers and more consistent risk treatment. It also gives teams a shared understanding of why decisions are made, which is essential as AI and automation become more embedded in lending workflows.
What should fintech leaders do next?
A unified view of the consumer is not built by adding one more tool or one more dataset. It requires a decisioning strategy that connects data, analytics, fraud, identity and product objectives. Fintech leaders should start by evaluating where their current decisioning frameworks fall short. Are credit and fraud signals looked at together? Are cash flow insights being used consistently? Are identity signals monitored after account opening? Are decisions explainable across teams and channels?
The 2026 State of Fintech Report recommends prioritizing experimentation tied to measurable decision lift and model performance. This means testing combinations of credit, alternative, cash flow and identity signals to determine where incremental data improves response rates, approval rates, early-loss reduction and fraud mitigation. It also means treating data and decisioning as connected priorities, with a focus on signal quality, integration and measurable impact. The goal is not to collect more inputs for the sake of volume. The goal is to understand which signals improve outcomes and how those signals should be applied at scale.
For fintechs, this is the next competitive frontier. Growth will continue to depend on product innovation, customer acquisition and speed to market. But the lenders that separate themselves will be the ones that can connect those growth priorities to a stronger decisioning foundation. That requires a consumer view that is broader than a credit profile, deeper than a fraud check and more actionable than a data warehouse. It requires a unified framework that helps lenders understand who the customer is, how the customer behaves and how risk may change over time.
Download the 2026 State of Fintech Report
The next phase of fintech will not be defined by a single innovation. It will be defined by the ability to connect identity, credit and behavioral data into more confident decisions across the full customer lifecycle. Fintechs that build this unified view will be better positioned to grow, manage risk and strengthen customer trust in a more complex market.
To explore the trends shaping fintech growth and decisioning in 2026, download Experian’s 2026 State of Fintech Report.
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To learn more about how Experian partners with fintechs, visit www.experian.com/fintech.
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Automation, artificial intelligence and machine learning are at the forefront of the continued digital transformation within the world of collections. And organizations from across industries — including healthcare, financial services and the public sector — are learning how automated debt collection can improve their workflows and strategies.
When implemented well, automation can ease pressure from call center agents, which can be especially important when there’s a tight labor market and retention is top of mind for every employer. Automated systems can also help improve recovery rates while minimizing the risk of human error and the corresponding liability.
These same systems can increase long-term customer satisfaction and lifetime value. Deeper insights into consumers’ financial situations and preferences allow you to avoid wasting resources and making contact when consumers are truly unable to pay. Instead, monitoring and following up with their preferred contact method can be a more successful approach — and a better experience for consumers.
Three tips for automated debt collection
Automation and artificial intelligence (AI) aren’t new to collections. You may have heard about or tried automated dialing systems, chatbots, text message services and virtual negotiators. But the following three points can be important to consider as the technology and compliance landscapes change.
1. Good automation depends on good data
Whether you’re using static automated systems to improve efficiencies or using a machine learning model that will adapt over time, the data you feed into the system needs to be accurate. The data can be internal, from call center agents and your customers, and external sources can help verify and expand on what you know.
With your internal systems, consider how you can automate processes to limit human errors. For example, you may be able to auto-fill contact information for customers and agents — saving them time and avoiding typos that can cause issues later.
External data sources can be helpful in several ways. You can use third-party data as a complementary resource to help determine the best address, phone number or email address to increase right-party contact (RPC) rates. External sources can also validate your internal data and automatically highlight errors or potentially outdated information, which can be important for maintaining compliance.
Robust and frequently updated datasets can make your collection efforts more efficient and effective. An automated system could be notified when a debtor resurfaces or gets a new job, triggering new reminders or requests for payment. And if you’re using the right tools, you can automatically route the account to internal or external servicing and prioritize accounts based on the consumer’s propensity to pay or the expected recovery amount.