In today’s financial services landscape, data is often dubbed the new oil, but unlike oil, its value increases when shared, not locked away. Yet financial institutions remain cautious about exchanging data. Regulatory constraints, competitive concerns, misaligned incentives, and inconsistent data quality are some of the key barriers slowing exchange of valuable data.
Despite these obstacles, a future where banks, insurers, regulators, and Fintechs collaborate on data - ethically, securely, and with the customer’s consent at the center - offers powerful advantages for all parties involved. From smarter fraud prevention and better customer service to more competitive offerings and new business models, data sharing isn’t just a nice-to-have, it’s a strategic imperative.
Sharing data across financial institutions sounds simple. Just exchange information, right? In reality, it’s complex:
Privacy and GDPR Constraints: Most financial data is inherently personal, i.e. transactional records, balances, account details, behavioral patterns, and even payment metadata can identify an individual. Under GDPR, such data can only be shared with explicit customer consent. This creates friction because consent must be specific, informed, and revocable, not a checkbox buried in a terms-and-conditions page.
Competition Concerns: Banks historically view their data as a key competitive differentiator. Revealing too much to peers can erode market advantage or create arbitrage opportunities. The instinct is not to share but to retain and protect.
Misaligned Incentives: In collaborative ecosystems, one bank may produce high-value data while another only consumes. Without proper compensation or incentive mechanisms, data producers may feel exploited. Mechanisms such as data credits, usage-based payment, or reciprocity models must be designed to ensure fair value exchange.
Data Quality and Standardization Challenges: Different banks label, define, and structure data differently. Without data harmonization (such as unified schemas and taxonomies) and without strict data quality controls (such as quality standards, quality audits and/or validation processes), any data exchange risks being unusable or even misleading.
To facilitate data sharing, a trusted governance layer is essential. Traditionally, this role is played by a centralized intermediary, an industry utility, consortium, or regulated platform that:
Ensures compliance with all regulations like GDPR
Provides standardized onboarding
Enforces ecosystem rules and data quality standards
Administers access controls and routing of data
Tracks usage and billing
Some technologists propose blockchain-based alternatives that reduce reliance on a single central custodian. While blockchain can enhance data integrity and transparency, real-world implementations often still require a governing entity to:
Operate the infrastructure
Resolve disputes
Ensure service-level reliability
Offer additional value-added services
Offer customer support
Furthermore, privacy-preserving technologies, such as Trusted Execution Environments (TEEs), confidential computing, secure multi-party computation (SMPC) and homomorphic encryption, can enable participants to process sensitive data without direct visibility into underlying personal information, while still extracting insights.
This hybrid architecture, combining trusted governance with advanced privacy tech, is where feasible, scalable data sharing begins.
Often regulatory frameworks are seen as obstacles to innovation, but in this case they can also create momentum for collaboration:
PSD2 / Open Banking: Forced banks to open payment account data with customer consent.
FiDA (Financial Data Access Laws): Push broader API-based sharing across financial services.
PSD3 / Instant Payments Directive: Encourages more real-time fraud data exchange.
Regulators are increasingly recognizing that siloed data weakens systemic fraud defenses and customer protection. They are nudging (or requiring) institutions to cooperate.
Still, institutions need to ensure benefits outweigh GDPR risk. Penalties for GDPR breaches are severe, so privacy-preserving data governance and risk-reward alignment must be carefully built.
While the challenges are significant, the potential rewards are even greater. Let’s explore concrete use cases where secure, collaborative data sharing already proves its value and where the future holds even more promise.
1. Anonymized Financial Crime Prevention
Fraud and money laundering flourish in data silos. Without a broader ecosystem view, financial institutions are often left blind to cross-institutional patterns that criminals exploit.
By securely sharing anonymized intelligence, such as suspicious account numbers, high-risk IP addresses, geolocations, device identifiers (e.g. MAC addresses), and dynamic fraud risk scores (e.g. exposed by the agent owning the account), institutions can significantly enhance their AML and KYC effectiveness.
Key benefits include:
Faster detection of emerging fraud and laundering techniques, leveraging collective insights rather than isolated investigations.
Cross-bank pattern recognition, enabling institutions to identify networks or behaviors that would remain invisible within a single data set.
Reduction in false positives, improving operational efficiency and avoiding unnecessary friction for legitimate customers.
Improved confidence scoring, based on real-time fraud status updates from across the ecosystem.
When combined with technologies like federated learning, institutions can even train shared fraud detection models without ever exposing raw data.
The result? A smarter, more responsive financial crime defense system delivering stronger compliance, lower financial crime losses, and a significantly better customer experience.
2. Smarter Payment Routing
Payments today often follow rigid, predefined routes, even when faster, cheaper, or more reliable options are available. This inefficiency stems from institutions making routing decisions in isolation, based on static rules or incomplete information.
By sharing anonymized performance data across institutions, including price, speed, success rates, and execution quality of different payment rails, financial institutions can create a dynamic, data-driven layer for smarter payment routing.
Similar to how Waze or Google Maps suggests optimal driving routes based on real-time traffic conditions, this collaborative intelligence would enable financial institutions to:
Estimate real-time expected completion times for each payment rail based on live performance metrics.
Select the fastest, most cost-effective, or most reliable route depending on customer preferences or transaction requirements.
Adapt to network issues, congestions, or maintenance windows by rerouting payments dynamically.
This approach not only enhances operational efficiency, but also:
Improves customer satisfaction, thanks to faster and more predictable payment experiences.
Reduces costs, by avoiding inefficient or expensive corridors.
Builds trust, as clients receive accurate ETAs and performance transparency for their payments.
Over time, this collective routing intelligence can evolve into a market utility for payment optimization, supporting banks, fintechs, and even corporates seeking better global payment experiences.
3. Real-Time End-to-End Payment Tracking
In a world where customers can track the location of a package in real time, it’s increasingly unacceptable that a payment, often far more critical, disappears into a black box the moment it leaves the initiating bank.
The question "Where is my payment?" continues to frustrate individuals and corporates alike. While internal visibility within a single bank may exist, transparency across banking networks is often missing. Once a payment exits one institution and enters another rail or intermediary, tracking becomes patchy or impossible.
Solutions like SWIFT gpi have brought partial relief but only for SWIFT-based payments. Many domestic, cross-border, or alternative rail payments remain invisible beyond the sending bank.
By sharing real-time transaction status updates across institutions, including both international networks and domestic clearing systems, financial institutions can enable true end-to-end payment visibility, from initiation to settlement.
Benefits of such a collaborative tracking layer include:
Full lifecycle transparency: Institutions can provide customers with up-to-date payment statuses, from initiation, processing, interbank routing, to final crediting.
Issue diagnosis and resolution: Delays or rejections can be traced quickly to the responsible party, reducing investigation cycles and operational workloads.
Enriched transaction metadata: Banks can share not just status, but also fees, corrections, enrichment data, or reasons for delays (e.g. compliance checks, insufficient info).
Customer-facing insights: Real-time notifications and expected delivery estimates can be surfaced to end users or corporate clients, enhancing trust and reducing support costs.
This model mirrors logistics tracking but tailored to finance, with data privacy, security, and regulatory compliance at its core.
When deployed across all payment types and institutions, such a solution transforms not only transparency but also trust in the financial system, turning the "black box" into a clear, connected payment journey.
4. Verification of Payee (VoP)
With the rise of authorized push payment (APP) fraud and misdirected transfers, Verification of Payee (VoP) has become an essential safeguard. It ensures that the name provided by a payer matches the actual account holder at the receiving bank, helping prevent mistakes and impersonation scams.
However, VoP services today are inconsistent. Some banks have real-time VoP checks, others rely on legacy systems, and in many countries, VoP isn’t available at all. Even when implemented, VoP often only works within a single domestic system, limiting its reach in cross-border scenarios.
This is where collaborative data sharing can elevate VoP to the next level.
By pooling anonymized transaction data and payment outcomes across institutions, a complementary verification layer can be created, one that analyzes:
Historical success rates of account number + name combinations
Patterns of failed or reversed payments due to mismatches
Anomalous payment behavior from known fraud vectors
This shared intelligence allows financial institutions to:
Augment existing VoP systems, especially in markets lacking central account referentials.
Improve match accuracy and reduce false positives.
Detect social engineering attempts or business email compromise (BEC) schemes, where fraudsters trick customers into paying to seemingly legitimate but altered accounts.
For institutions without real-time VoP systems, this networked approach can act as a lightweight, intelligence-based substitute. For others, it becomes a secondary fraud-detection signal, flagging unusual behavior even when names appear to match.
As fraudsters become more sophisticated, VoP must evolve from a static lookup tool to a dynamic, ecosystem-driven risk signal, powered by collective insights and continuously refreshed data.
5. Cashback, Coupons & Retail Rewards
As competition for consumer attention intensifies, both retailers and financial institutions are seeking smarter ways to build loyalty. One of the most promising and underutilized approaches lies in cross-industry data collaboration to power personalized cashback and coupon programs.
At the core of this model is the integration of transaction data (from banks) and receipt-level data (from retailers). When combined (with customer consent) this data fusion allows for:
Automated, context-specific cashback at the point of payment.
Targeted offers based on actual purchasing behavior, not generic demographics.
Cross-retailer insights, enabling shared benefits and benchmarking.
Real-time promotional redemption, linked directly to a payment card.
Here’s how it might work in practice:
A customer pays with their bank card at a participating retailer.
The bank recognizes the merchant and product category in real time.
The system applies a relevant cashback or coupon offer instantly, no manual activation needed.
The reward is credited back automatically, and insights flow to both the bank and the retailer.
Benefits for stakeholders include:
Retailers gain more traffic, better attribution for promotions, and richer analytics on consumer trends.
Banks increase card usage, customer engagement, and loyalty through differentiated services.
Consumers enjoy seamless, frictionless rewards, without extra apps or manual redemption steps.
Beyond individual rewards, this approach can also inform macroeconomic analysis, inflation monitoring, and consumer sentiment tracking, making it valuable for regulators and policymakers.
Ultimately, cashback and coupon systems driven by shared, anonymized transaction data represent a win-win-win, for consumers, for banks, and for the retail economy.
6. Shared KYC / KYB Utility
Customer onboarding is one of the most repetitive and resource-intensive processes in financial services. Every institution collects largely the same information for Know Your Customer (KYC) and Know Your Business (KYB) procedures, creating friction for customers and redundant effort for institutions.
In a world where data privacy, operational efficiency, and regulatory compliance are all paramount, a shared KYC/KYB utility offers a compelling solution. Through secure, consent-based data sharing, financial institutions can collaborate to reduce duplication, improve data quality, and simplify compliance.
Here’s how it works:
Customers (individuals or businesses) consent to share their verified identity and business data with multiple institutions.
Financial institutions access standardized, validated information through a trusted intermediary or privacy-preserving framework.
Updates to key data, such as addresses, legal representatives, or beneficial owners, propagate across the ecosystem, ensuring accuracy and timeliness.
Each data consumer applies its own risk models and due diligence procedures, but without needing to recollect baseline information from scratch.
The benefits are substantial:
Faster onboarding and servicing, reducing time-to-market for new customers.
Lower operational costs, by eliminating repetitive data capture and verification.
Improved customer experience, with fewer touchpoints and document requests.
Stronger compliance, thanks to consistent, auditable data sources and reduced error risk.
To make this work, however, institutions must align on:
Common data standards and formats.
Interpretations of regulatory requirements, such as what data is "sufficient" for due diligence.
Robust consent and access management, to ensure customers control who sees their data.
Clear accountability for data accuracy and updates.
Some regions are already experimenting with shared KYC models, especially for corporate clients or cross-border onboarding. But broad adoption will require trust frameworks, regulatory clarity, and a clear incentive model for all participants.
Ultimately, a shared KYC/KYB layer could become a utility as essential as credit bureaus, improving security, transparency, and ease of doing business across the financial landscape.
7. Shared Securities Master Data & Calculations
Managing securities data is a foundational but often fragmented task in financial institutions. Every bank maintains its own Securities Master File, aggregating reference data from providers like Bloomberg, Telekurs, S&P, and others. Yet despite the cost and effort, data quality issues persist, missing fields, inconsistent attributes, or conflicting identifiers are common.
What’s more, financial institutions often duplicate the same quality controls and corrections across their systems, checking for:
Missing ISINs or CUSIPs
Incomplete instrument descriptions
Incorrect coupon structures or maturity dates
Inconsistent ratings or classifications
Redundant or misaligned corporate actions
…
This redundancy is inefficient, expensive, and error-prone. Worse, it leads to inconsistent downstream calculations, impacting risk reporting, regulatory compliance, and even trading decisions.
By shifting toward a collaborative data validation and sharing model, the industry could dramatically improve accuracy and reduce waste.
Key components of such a model include:
Shared corrections and enrichment layers, allowing institutions to upload and access verified fixes or computed fields.
Collective data quality controls, where multiple institutions flag and validate anomalies in provider feeds.
Standardized calculations, such as Value at Risk (VaR), duration, yield-to-maturity (YTM), or convexity, computed once and reused, rather than repeated individually by each institution.
This would not only reduce cost but also:
Enhance regulatory reporting consistency, especially for capital adequacy and risk metrics.
Improve pricing and valuation accuracy, particularly for complex or illiquid instruments.
Accelerate time-to-market for new instruments, with fewer manual interventions.
Naturally, this raises governance questions. Data vendors (like Bloomberg and Telekurs) must be compensated appropriately, particularly if one utility aggregates multiple feeds. Access controls, usage tracking, and rights management become crucial to ensure contractual compliance and protect proprietary inputs.
Still, the potential gains are enormous. A well-designed securities data collaboration layer can help the industry move from fragmented reconciliation to shared reliability, freeing resources, reducing risk, and elevating confidence in the data that underpins nearly every financial transaction.
8. Proxy Account & Identity Resolution
As digital payments evolve, traditional account numbers like IBANs are no longer the primary identifiers in many systems. Increasingly, users send and receive money using proxies, such as phone numbers, email addresses, national IDs, or business identifiers like VAT numbers.
This trend improves user experience but introduces new complexity in account resolution. Behind every proxy must sit a validated account number and an identity, securely linked, up to date, and instantly retrievable.
To make this work at scale, financial institutions need to collaborate through centralized or federated repositories that manage proxy-to-account mappings. These repositories, operated by trusted intermediaries or under regulatory oversight, allow institutions to:
Resolve proxies in real time, translating them into valid destination accounts.
Maintain consistent referential integrity, even when customers change banks or identifiers.
Prevent fraud and spoofing, by verifying ownership and avoiding duplicate registrations.
Beyond payments, this proxy infrastructure supports:
Request to pay (R2P) flows
Invoice automation and reconciliation
Context-aware transactions, such as donations, subscriptions, or loyalty payments
Sharing proxy mappings across institutions, under strict privacy, consent, and usage controls, ensures seamless interoperability and network-wide user experience improvements.
9. Collaborative Credit Scoring & Financial Insights
Traditional credit scoring models rely heavily on siloed data, such as loan repayment histories or credit card usage, often limited to the institution holding the customer relationship. But this narrow view risks missing vital information, especially for new-to-credit individuals, small businesses, or cross-border applicants.
By collaborating across institutions, with proper consent and privacy controls, financial institutions can build a richer, more accurate picture of creditworthiness.
When data from multiple sources is combined, credit scoring can evolve to incorporate:
Transaction behavior across accounts (e.g. income patterns, spending signals, repayment reliability).
Cash flow analytics, especially for SMEs that lack formal credit history.
Asset and liability visibility, offering a more complete customer perspective.
Behavioral indicators, such as payment consistency, seasonality, and saving habits.
The advantages of this collaborative approach are clear:
More inclusive lending, enabling underserved or thin-file customers to access credit.
Better risk segmentation, allowing lenders to price products more accurately and reduce defaults.
Faster credit decisions, especially in real-time credit approvals or digital onboarding.
Stronger compliance with responsible lending frameworks, based on more comprehensive data.
These insights are not limited to lending. They also power:
Holistic financial advisory services, aligned with the customer’s real-world financial profile.
Proactive life-event engagement, such as home buying, retirement planning, or major purchases.
Cross-institution financial wellness tools, helping users understand and optimize their financial situation.
10. Asset Information Sharing
For many financial products, mortgages, car loans, insurance policies, having accurate, up-to-date information about the underlying asset is critical. Whether it’s a vehicle, a house, or another high-value item, institutions need clarity on:
Ownership and co-ownership
Current market value
Risk factors (e.g. flood zones, crime levels, insurance history)
Existing usage as collateral or insurance coverage
Today, much of this data is scattered across registries, insurers, real estate databases, and internal bank systems. The result? A fragmented, inefficient, and often manual process that slows decision-making and introduces risk.
By sharing verified asset data through trusted intermediaries or collaborative frameworks, financial institutions, insurers, and relevant public entities can:
Accelerate loan origination, with quicker validation of collateral value and ownership
Improve underwriting accuracy, for both property and vehicle insurance
Refine loan-to-value (LTV) assessments
Reduce fraud and document manipulation risks, especially in high-value transactions
Simplify claims processing, when all stakeholders operate from a shared data source
For example:
A car’s current value, registration status, accident history, and insurance coverage can be verified instantly during loan or lease approval.
A home buyer’s bank can access up-to-date property valuations, ownership records, and zoning risks to streamline mortgage approval and reduce default exposure.
An insurer can access credit bureau and transaction data (with consent) to align premiums more accurately with the customer’s financial profile and asset value.
11. Financial Situation & Life Event Insights
Understanding a customer’s full financial situation, not just within one institution, but across their entire financial footprint, is the cornerstone of meaningful financial advice, responsible lending, and tailored service delivery.
Yet, most banks still operate with partial visibility. They see only the accounts, transactions, and products they manage, while valuable context sits locked in other banks, insurers, investment platforms, payroll systems, or tax records.
Through secure, consent-based data sharing, financial institutions can gain access to a broader, richer picture of a customer’s financial health, including:
Income patterns and salary inflows.
Recurring obligations (e.g. rent, loans, alimony).
Asset holdings (e.g. savings, investments, properties).
Outstanding liabilities (e.g. mortgages, personal loans, credit card debt).
Business revenue and financial statements for entrepreneurs.
Indicators of life events (e.g. maternity benefits, pension transitions, inheritance inflows).
With this data, institutions can deliver:
Personalized financial planning, aligned with real-world needs and goals
Proactive outreach tied to key life events, from starting a family or changing jobs to retiring or selling a business
Responsible lending, based on actual affordability rather than static credit scores
Optimized investment advice, tailored to the customer’s risk capacity, liquidity needs, and future plans
For example:
A customer receiving childcare subsidies may benefit from tailored savings plans or adjusted loan offers.
A freelancer showing stable but seasonal income could be offered a flexible credit line instead of being declined by rigid underwriting models.
A household approaching retirement might receive automatic prompts to review long-term care insurance or annuity options.
12. Counterparty Risk Scoring
In today’s economy, businesses and individuals increasingly interact with unknown counterparties, whether renting a property, signing a contract, hiring a service provider, or engaging in peer-to-peer transactions. Yet trust remains hard to quantify and even harder to verify.
What if banks and financial institutions, already holding deep, regulated insights into solvency, liquidity, and behavior, could help solve this challenge?
Through secure, GDPR-compliant data sharing and consent-driven scoring systems, financial institutions can help build a new layer of quantitative trust, offering verified, privacy-respecting signals about:
Solvency: The counterparty’s overall wealth, based on assets minus liabilities.
Liquidity: The ability to meet obligations in the short term.
Trustworthiness: A more nuanced signal, combining financial health, behavioral history, and risk profiles.
Institutions may even allow peer ratings or offer tools for counterparties to request improvement suggestions to enhance their credibility scoring.
Here are some example applications:
A homeowner rents out a property and requests a verified ability-to-pay certificate from the prospective tenant’s bank, without seeing private salary details.
A small business seeks to engage a contractor for a high-value project and checks the financial resilience of the firm before signing.
A buyer presents a non-binding creditworthiness certificatebefore entering a real estate purchase, reducing failed sales due to mortgage refusals.
A P2P lending platform integrates real-time risk scores from banks to assess borrower quality, without exposing sensitive identity data.
A supplier verifies a prospective client’s payment reliability, improving confidence in entering long-term agreements.
To realize these gains in each of those example use cases, financial institutions will need to:
Develop governance frameworks that align consent, privacy, and liability.
Invest in privacy-preserving technology (TEEs, confidential computing, encryption).
Define fair incentive models, including compensation for data producers.
Standardize data definitions and quality controls across shared use cases.
Collaborate with regulators to shape interoperable, compliant ecosystems.
The financial services industry stands at a crossroads: continue guarding data as a fortress, or unlock shared value through responsible collaboration.
Data sharing doesn’t mean giving away competitive advantage. It means participating in ecosystems where privacy is respected, risk is managed, and value flows to all stakeholders from banks and fintechs to customers and regulators. In an age where data can drive smarter decisions, stronger defenses, and better customer experiences, the real question isn’t if we should share data, but how soon can we start?
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