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A Quick Start Guide to the Payment Services Directive (PSD2)
In 2023, there were 266.2 billion real-time payments indicating that the demand for secure transactions has never been higher. As we move towards a more open banking system, there are a host of new payment solutions that offer convenience and efficiency, but they also present new risks.
The Payment Services Directive 2 (PSD2) is one of many regulations established to address these concerns. PSD2 is a European Union (EU) business initiative to offer smooth payment experiences while helping customers feel safe from online threats.
In this post, learn what PSD2 includes, how it improves security for online payments, and how Matomo supports banks and financial institutions with PSD2 compliance.
What is PSD2?
PSD2 is an EU directive that aims to improve the security of electronic payments across the EU. It enforces strong customer authentication and allows third-party access to consumer accounts with explicit consent.
Its main objectives are:
- Strengthening security and data privacy measures around digital payments.
- Encouraging innovation by allowing third-party providers access to banking data.
- Improving transparency with clear communication regarding fees, terms and conditions associated with payment services.
- Establishing a framework for sharing customer data securely through APIs for PSD2 open banking.
Rationale behind PSD2
PSD2’s primary purpose is to engineer a more integrated and efficient European payment market without compromising the security of online transactions.
The original directive aimed to standardise payment services across EU member states, but as technology evolved, an updated version was needed.
PSD2 is mandatory for various entities within the European Economic Area (EEA), like:
- Banks and credit institutions
- Electronic money institutions or digital banks like Revolut
- Card issuing and acquiring institutions
- Fintech companies
- Multi-national organisations operating in the EU
PSD2 implementation timeline
With several important milestones, PSD2 has reshaped how payment services work in Europe. Here’s a closer look at the pivotal events that paved the way for its launch.
- 2002: The banking industry creates the European Payments Council (EC), which drives the Single Euro Payments Area (SEPA) initiative to include non-cash payment instruments across European regions.
- 2007: PSD1 goes into effect.
- 2013: EC proposes PSD2 to include protocols for upcoming payment services.
- 2015: The Council of European Union passes PSD2 and gives member states two years to incorporate it.
- 2018: PSD2 goes into effect.
- 2019: The final deadline for all companies within the EU to comply with PSD2’s regulations and rules for strong customer authentication.
PSD2: Key components
PSD2 introduces several key components. Let’s take a look at each one.
Strong Customer Authentication (SCA)
The Regulatory Technical Standards (RTS) under PSD2 outline specific requirements for SCA.
SCA requires multi-factor authentication for online transactions. When customers make a payment online, they need to verify their identity using at least two of the three following elements:
- Knowledge: Something they know (like a password, a code or a secret answer)
- Possession: Something they have (like their phone or card)
- Inherence: Something they are (like biometrics — fingerprints or facial features)
Before SCA, banks verified an individual’s identity only using a password. This dual verification allows only authorised users to complete transactions. SCA implementation reduces fraud and increases the security of electronic payments.
SCA implementation varies for different payment methods. Debit and credit cards use the 3D Secure (3DS) protocol. E-wallets and other local payment measures often have their own SCA-compliant steps.
3DS is an extra step to authenticate a customer’s identity. Most European debit and credit card companies implement it. Also, in case of fraudulent chargebacks, the issuing bank becomes liable due to 3DS, not the business.
However, in SCA, certain transactions are exempt:
- Low-risk transactions: A transaction by an issuer or an acquirer whose fraud level is below a specific threshold. If the acquirer feels that a transaction is low risk, they can request to skip SCA.
- Low-value transactions: Transactions under €30.
- Trusted beneficiaries: Trusted merchants customers choose to safelist.
- Recurring payments: Recurring transactions for a fixed amount are exempt from SCA after the first transaction.
Third-party payment service providers (TPPs) framework
TPPs are entities authorised to access customer banking data and initiate payments. There are three types of TPPs:
Account Information Service Providers (AISPs)
AISPs are services that can view customers’ account details, but only with their permission. For example, a budgeting app might use AISP services to gather transaction data from a user’s bank account, helping them monitor expenses and oversee finances.
Payment Initiation Service Providers (PISPs)
PISPs enable clients to initiate payments directly from their bank accounts, bypassing the need for conventional payment options such as debit or credit cards. After the customer makes a payment, PISPs immediately contact the merchant to ensure the user can access the online services or products they bought.
Card-Based Payment Instruments (CBPII)
CBPIIs refer to services that issue payment cards linked to customer accounts.
Requirements for TPPs
To operate effectively under PSD2, TPPs must meet several requirements:
Consumer consent: Customers must explicitly authorise TPPs to retrieve their financial data. This way, users can control who can view their information and for what purpose.
Security compliance: TPPs must follow SCA and secure communication guidelines to protect users from fraud and unauthorised access.
API availability: Banks must make their Application Programming Interfaces (APIs) accessible and allow TPPs to connect securely with the bank’s systems. This availability helps in easy integration and lets TPPs access essential data.
Consumer protection methods
PSD2 implements various consumer protection measures to increase trust and transparency between consumers and financial institutions. Here’s a closer look at some of these key methods:
- Prohibition of unjustified fees: PSD2 requires banks to clearly communicate any additional charges or fees for international transfers or account maintenance. This ensures consumers are fully aware of the actual costs and charges.
- Timely complaint resolution: PSD2 mandates that payment service providers (PSPs) have a straightforward complaint procedure. If a customer faces any problems, the provider must respond within 15 business days. This requirement encourages consumers to engage more confidently with financial services.
- Refund in case of unauthorised payment: Customers are entitled to a full refund for payments made without their consent.
- Surcharge ban: Additional charges on credit and debit card payments aren’t allowed. Businesses can’t impose extra fees on these payment methods, which increases customers’ purchasing power.
Benefits of PSD2
Businesses — particularly those in banking, fintech, finserv, etc. — stand to benefit from PSD2 in several ways.
Access to customer data
With customer consent, banks can analyse spending patterns to develop tailored financial products that match customer needs, from personalised savings accounts to more relevant loan offerings.
Innovation and cost benefits
PSD2 opened payment processing up to more market competition. New payment companies bring fresh approaches to banking services, making daily transactions more efficient while driving down processing fees across the sector.
Also, banks now work alongside payment technology providers, combining their strengths to create better services. This collaboration brings faster payment options to businesses, helping them stay competitive while reducing operational costs.
Improved customer trust and experience
Due to PSD2 guidelines, modern systems handle transactions quickly without compromising the safety of payment data, creating a balanced approach to digital banking.
Banking customers now have more control over their financial information. Clear processes allow consumers to view and adjust their financial preferences as needed.
Strong security standards form the foundation of these new payment systems. Payment provider platforms must adhere to strict regulations and implement additional protection measures.
Challenges in PSD2 compliance
What challenges can banks and financial institutions face regarding PSD2 compliance? Let’s examine them.
Resource requirements
For many businesses, the new requirements come with a high price tag. PSD2 requires banks and fintechs to build and update their systems so that other providers can access customer data safely. For example, they must develop APIs to allow TPPs to acquire customer data.
Many banks still use older systems that can’t meet PSD2’s added requirements. In addition to the cost of upgrades, complying with PSD2 requires banks to devote resources to training staff and monitoring compliance.
The significant costs required to update legacy systems and IT infrastructure while keeping services running remain challenging.
Risks and penalties
Organisations that fail to comply with PSD2 regulations can face significant penalties.
Additionally, the overlapping requirements of PSD2 and other regulations, such as the General Data Protection Regulation (GDPR), can create confusion.
Banks need clear agreements with TPPs about who’s responsible when things go wrong. This includes handling data breaches, preventing data misuse and protecting customer information.
Increased competition
Introducing new players in the financial ecosystem, such as AISPs and PISPs, creates competition. Banks must adapt their services to stay competitive while managing compliance costs.
PSD2 aims to protect customers but the stronger authentication requirements can make banking less convenient. Banks must balance security with user experience. Focused time, effort and continuous monitoring are needed for businesses to stay compliant and competitive.
How Matomo can help
Matomo gives banks and financial institutions complete control over their data through privacy-focused web analytics, keeping collected information internal rather than being used for marketing or other purposes.
Its advanced security setup includes access controls, audit logs, SSL encryption, single sign-on and two-factor authentication. This creates a secure environment where sensitive data remains accessible only to authorised staff.
While prioritizing privacy, Matomo provides tools to understand user flow and customer segments, such as session recordings, heatmaps and A/B testing.
Financial institutions particularly benefit from several key features:
- Tools for obtaining explicit consent before processing personal data like this Do Not Track preference
- Insights into how financial institutions integrate TPPs (including API usage, user engagement and potential authentication drop-off points)
- Tracking of failed login attempts or unusual access patterns
- IP anonymization to analyse traffic patterns and detect potential fraud
PSD3: The next step
In recent years, we have seen the rise of innovative payment companies and increasingly clever fraud schemes. This has prompted regulators to propose updates to payment rules.
PSD3’s scope is to adapt to the evolving digital transformation and to better handle these fraud risks. The proposed measures:
- Encourage PSPs to share fraud-related information.
- Make customers aware of the different types of fraud.
- Strengthen customer authentication standards.
- Provide non-bank PSPs restricted access to EU payment systems.
- Enact payment rules in a directly applicable regulation and harmonise and enforce the directive.
Web analytics that respect user privacy
Achieving compliance with PSD2 may be a long road for some businesses. With Matomo, organisations can enjoy peace of mind knowing their data practices align with legal requirements.
Ready to stop worrying over compliance with regulations like PSD2 and take control of your data? Start your 21-day free trial with Matomo.
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Join us at MatomoCamp 2024 world tour edition
13 novembre 2024, par Daniel Crough — UncategorizedJoin us at MatomoCamp 2024 world tour edition, our online conference dedicated to Matomo Analytics—the leading open-source web analytics platform that prioritises data privacy.
- 🗓️ Date: 14 November 2024
- 🌐 Format: 24-hour virtual conference accessible worldwide
- 💰 Cost: Free and no need to register
Event highlights
Opening ceremony
Begin the day with a welcome from Ronan Chardonneau, co-organiser of MatomoCamp and customer success manager at Matomo.
Keynote: “Matomo by its creator”
Attend a special session with Matthieu Aubry, the founder of Matomo Analytics. Learn about the platform’s evolution and future developments.
Explore MatomoCamp 2024’s diverse tracks and topics
MatomoCamp 2024 offers a wide range of topics across several tracks, including using Matomo, integration, digital analytics, privacy, plugin development, system administration, business, other free analytics, use cases, and workshops and panel talks.
Featured sessions
1. Using AI to fetch raw data with Python
Speaker: Ralph Conti
Time: 14 November, 12:00 PM UTCDiscover how to combine AI and Matomo’s API to create unique reporting solutions. Leverage Python for advanced data analysis and unlock new possibilities in your analytics workflow.
2. Supercharge Matomo event tracking with custom reports
Speaker: Thomas Steur
Time: 14 November, 2:00 PM UTCLearn how to enhance event tracking and simplify data analysis using Matomo’s custom reports feature. This session will help you unlock the full potential of your event data.
3. GDPR with AI and AI Act
Speaker: Stefanie Bauer
Time: 14 November, 4:00 PM UTCNavigate the complexities of data protection requirements for AI systems under GDPR. Explore the implications of the new AI Act and receive practical tips for compliance.
4. A new data mesh era!
Speaker: Jorge Powers
Time: 14 November, 4:00 PM UTCExplore how Matomo supports the data mesh approach, enabling decentralised data ownership and privacy-focused analytics. Learn how to empower teams to manage and analyse data without third-party reliance.
5. Why Matomo has to create a MTM server side: The future of data privacy and user tracking
Panel discussion
Time: 14 November, 6:00 PM UTCJoin experts in a discussion on the necessity of server-side tag management for enhanced privacy and compliance. Delve into the future of data privacy and user tracking.
6. Visualisation of Matomo data using external tools
Speaker: Leticia Rodríguez Morado
Time: 14 November, 8:00 PM UTCLearn how to create compelling dashboards using Grafana and Matomo data. Enhance your data visualisation skills and gain better insights.
7. Keep it simple: Tracking what matters with Matomo
Speaker: Scott Fillman
Time: 14 November, 9:00 PM UTCDiscover how to focus on essential metrics and simplify your analytics setup for more effective insights. Learn tactics for a powerful, streamlined Matomo configuration.
Stay connected
Stay updated with the latest news and announcements:
- Twitter: @MatomoCamp
- YouTube: @MatomoCamp
- Official website: matomocamp.org
Don’t miss out
MatomoCamp 2024 world tour edition is more than a conference; it’s a global gathering shaping the future of ethical analytics. Whether you aim to enhance your skills, stay informed about industry trends, or network with professionals worldwide, this event offers valuable opportunities.
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How HSBC and ING are transforming banking with AI
We recently partnered with FinTech Futures to produce an exciting webinar discussing how analytics leaders from two global banks are using AI to protect customers, streamline operations, and support environmental goals.
Watch the on-demand webinar: Advancing analytics maturity.
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</script>Meet the expert panel
Roshini Johri heads ESG Analytics at HSBC, where she leads AI and remote sensing applications supporting the bank’s net zero goals. Her expertise spans climate tech and financial services, with a focus on scalable analytics solutions.
Marco Li Mandri leads Advanced Analytics Strategy at ING, where he focuses on delivering high-impact solutions and strengthening analytics foundations. His background combines analytics, KYC operations, and AI strategy.
Carmen Soini Tourres works as a Web Analyst Consultant at Matomo, helping financial organisations optimise their digital presence whilst maintaining privacy compliance.Key findings from the webinar
The discussion highlighted four essential elements for advancing analytics capabilities:
1. Strong data foundations matter most
“It doesn’t matter how good the AI model is. It is garbage in, garbage out,”
Johri explained. Banks need robust data governance that works across different regulatory environments.
2. Transform rather than tweak
Li Mandri emphasised the need to reconsider entire processes:
“We try to look at the banking domain and processes and try to re-imagine how they should be done with AI.”
3. Bridge technical and business understanding
Both leaders stressed the value of analytics translators who understand both technology and business needs.
“We’re investing in this layer we call product leads,”
Li Mandri explained. These roles combine technical knowledge with business acumen – a rare but vital skill set.
4. Consider production costs early
Moving from proof-of-concept to production requires careful planning. As Johri noted:
“The scale of doing things in production is quite massive and often doesn’t get accounted for in the cost.”
This includes:
- Ongoing monitoring requirements
- Maintenance needs
- Regulatory compliance checks
- Regular model updates
Real-world applications
ING’s approach demonstrates how banks can transform their operations through thoughtful AI implementation. Li Mandri shared several areas where the bank has successfully deployed analytics solutions, each benefiting both the bank and its customers.
Customer experience enhancement
The bank’s implementation of AI-powered instant loan processing shows how analytics can transform traditional banking.
“We know AI can make loans instant for the customer, that’s great. Clicking one button and adding a loan, that really changes things,”
Li Mandri explained. This goes beyond automation – it represents a fundamental shift in how banks serve their customers.
The system analyses customer data to make rapid lending decisions while maintaining strong risk assessment standards. For customers, this means no more lengthy waiting periods or complex applications. For the bank, it means more efficient resource use and better risk management.
The bank also uses AI to personalise customer communications.
“We’re using that to make certain campaigns more personalised, having a certain tone of voice,”
noted Li Mandri. This particularly resonates with younger customers who expect relevant, personalised interactions from their bank.
Operational efficiency transformation
ING’s approach to Know Your Customer (KYC) processes shows how AI can transform resource-heavy operations.
“KYC is a big area of cost for the bank. So we see massive value there, a lot of scale,”
Li Mandri explained. The bank developed an AI-powered system that:
- Automates document verification
- Flags potential compliance issues for human review
- Maintains consistent standards across jurisdictions
- Reduces processing time while improving accuracy
This implementation required careful consideration of regulations across different markets. The bank developed monitoring systems to ensure their AI models maintain high accuracy while meeting compliance standards.
In the back office, ING uses AI to extract and process data from various documents, significantly reducing manual work. This automation lets staff focus on complex tasks requiring human judgment.
Sustainable finance initiatives
ING’s commitment to sustainable banking has driven innovative uses of AI in environmental assessment.
“We have this ambition to be a sustainable bank. If you want to be a sustainable finance customer, that requires a lot of work to understand who the company is, always comparing against its peers.”
The bank developed AI models that:
- Analyse company sustainability metrics
- Compare environmental performance against industry benchmarks
- Assess transition plans for high-emission industries
- Monitor ongoing compliance with sustainability commitments
This system helps staff evaluate the environmental impact of potential deals quickly and accurately.
“We are using AI there to help our frontline process customers to see how green that deal might be and then use that as a decision point,”
Li Mandri noted.
HSBC’s innovative approach
Under Johri’s leadership, HSBC has developed several groundbreaking uses of AI and analytics, particularly in environmental monitoring and operational efficiency. Their work shows how banks can use advanced technology to address complex global challenges while meeting regulatory requirements.
Environmental monitoring through advanced technology
HSBC uses computer vision and satellite imagery analysis to measure environmental impact with new precision.
“This is another big research area where we look at satellite images and we do what is called remote sensing, which is the study of a remote area,”
Johri explained.
The system provides several key capabilities:
- Analysis of forest coverage and deforestation rates
- Assessment of biodiversity impact in specific regions
- Monitoring of environmental changes over time
- Measurement of environmental risk in lending portfolios
“We can look at distant images of forest areas and understand how much percentage deforestation is being caused in that area, and we can then measure our biodiversity impact more accurately,”
Johri noted. This technology enables HSBC to:
- Make informed lending decisions
- Monitor environmental commitments of borrowers
- Support sustainability-linked lending programmes
- Provide accurate environmental impact reporting
Transforming document analysis
HSBC is tackling one of banking’s most time-consuming challenges: processing vast amounts of documentation.
“Can we reduce the onus of human having to go and read 200 pages of sustainability reports each time to extract answers?”
Johri asked. Their solution combines several AI technologies to make this process more efficient while maintaining accuracy.
The bank’s approach includes:
- Natural language processing to understand complex documents
- Machine learning models to extract relevant information
- Validation systems to ensure accuracy
- Integration with existing compliance frameworks
“We’re exploring solutions to improve our reporting, but we need to do it in a safe, robust and transparent way.”
This careful balance between efficiency and accuracy exemplifies HSBC’s approach to AI.
Building future-ready analytics capabilities
Both banks emphasise that successful analytics requires a comprehensive, long-term approach. Their experiences highlight several critical considerations for financial institutions looking to advance their analytics capabilities.
Developing clear governance frameworks
“Understanding your AI risk appetite is crucial because banking is a highly regulated environment,”
Johri emphasised. Banks need to establish governance structures that:
- Define acceptable uses for AI
- Establish monitoring and control mechanisms
- Ensure compliance with evolving regulations
- Maintain transparency in AI decision-making
Creating solutions that scale
Li Mandri stressed the importance of building systems that grow with the organisation:
“When you try to prototype a model, you have to take care about the data safety, ethical consideration, you have to identify a way to monitor that model. You need model standard governance.”
Successful scaling requires:
- Standard approaches to model development
- Clear evaluation frameworks
- Simple processes for model updates
- Strong monitoring systems
- Regular performance reviews
Investing in people and skills
Both leaders highlighted how important skilled people are to analytics success.
“Having a good hiring strategy as well as creating that data literacy is really important,”
Johri noted. Banks need to:
- Develop comprehensive training programmes
- Create clear career paths for analytics professionals
- Foster collaboration between technical and business teams
- Build internal expertise in emerging technologies
Planning for the future
Looking ahead, both banks are preparing for increased regulation and growing demands for transparency. Key focus areas include:
- Adapting to new privacy regulations
- Making AI decisions more explainable
- Improving data quality and governance
- Strengthening cybersecurity measures
Practical steps for financial institutions
The experiences shared by HSBC and ING provide valuable insights for financial institutions at any stage of their analytics journey. Their successes and challenges outline a clear path forward.
Key steps for success
Financial institutions looking to enhance their analytics capabilities should:
- Start with strong foundations
- Invest in clear data governance frameworks
- Set data quality standards
- Build thorough documentation processes
- Create transparent data tracking
- Think strategically about AI implementation
- Focus on transformative rather than small changes
- Consider the full costs of AI projects
- Build solutions that can grow
- Balance innovation with risk management
- Invest in people and processes
- Develop internal analytics expertise
- Create clear paths for career growth
- Foster collaboration between technical and business teams
- Build a culture of data literacy
- Plan for scale
- Establish monitoring systems
- Create governance frameworks
- Develop standard approaches to model development
- Stay flexible for future regulatory changes
Learn more
Want to hear more insights from these industry leaders? Watch the complete webinar recording on demand. You’ll learn:
- Detailed technical insights from both banks
- Extended Q&A with the speakers
- Additional case studies and examples
- Practical implementation advice
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Watch the on-demand webinar: Advancing analytics maturity.
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Banking Data Strategies – A Primer to Zero-party, First-party, Second-party and Third-party data
Banks hold some of our most sensitive information. Every transaction, loan application, and account balance tells a story about their customers’ lives. Under GDPR and banking regulations, protecting this information isn’t optional – it’s essential.
Yet banks also need to understand how customers use their services to serve them better. The solution lies in understanding different types of banking data and how to handle each responsibly. From direct customer interactions to market research, each data source serves a specific purpose and requires its own privacy controls.
Before diving into how banks can use each type of data effectively, let’s look into the key differences between them:
Data Type What It Is Banking Example Legal Considerations First-party Data from direct customer interactions with your services Transaction records, service usage patterns Different legal bases apply (contract, legal obligation, legitimate interests) Zero-party Information customers actively provide Stated preferences, financial goals Requires specific legal basis despite being voluntary; may involve profiling Second-party Data shared through formal partnerships Insurance history from partners Must comply with PSD2 and specific data sharing regulations Third-party Data from external providers Market analysis, demographic data Requires due diligence on sources and specific transparency measures What is first-party data?
First-party data reveals how customers actually use your banking services. When someone logs into online banking, withdraws money from an ATM, or speaks with customer service, they create valuable information about real banking habits.
This direct interaction data proves more reliable than assumptions or market research because it shows genuine customer behaviour. Banks need specific legal grounds to process this information. Basic banking services fall under contractual necessity, while fraud detection is required by law. Marketing activities need explicit customer consent. The key is being transparent with customers about what information you process and why.
Start by collecting only what you need for each specific purpose. Store information securely and give customers clear control through privacy settings. This approach builds trust while helping meet privacy requirements under the GDPR’s data minimisation principle.
What is zero-party data?
Zero-party data emerges when customers actively share information about their financial goals and preferences. Unlike first-party data, which comes from observing customer behaviour, zero-party data comes through direct communication. Customers might share their retirement plans, communication preferences, or feedback about services.
Interactive tools create natural opportunities for this exchange. A retirement calculator helps customers plan their future while revealing their financial goals. Budget planners offer immediate value through personalised advice. When customers see clear benefits, they’re more likely to share their preferences.
However, voluntary sharing doesn’t mean unrestricted use. The ICO’s guidance on purpose limitation applies even to freely shared information. Tell customers exactly how you’ll use their data, document specific reasons for collecting each piece of information, and make it simple to update or remove personal data.
Regular reviews help ensure you still need the information customers have shared. This aligns with both GDPR requirements and customer expectations about data management. By treating voluntary information with the same care as other customer data, banks build lasting trust.
What is second-party data?
Second-party data comes from formal partnerships between banks and trusted companies. For example, a bank might work with an insurance provider to better understand shared customers’ financial needs.
These partnerships need careful planning to protect customer privacy. The ICO’s Data Sharing Code provides clear guidelines: both organisations must agree on what data they’ll share, how they’ll protect it, and how long they’ll keep it before any sharing begins.
Transparency builds trust in these arrangements. Tell customers about planned data sharing before it happens. Explain what information you’ll share and how it helps provide better services.
Regular audits help ensure both partners maintain high privacy standards. Review shared data regularly to confirm it’s still necessary and properly protected. Be ready to adjust or end partnerships if privacy standards slip. Remember that your responsibility to protect customer data extends to information shared with partners.
Successful partnerships balance improved service with diligent privacy protection. When done right, they help banks understand customer needs better while maintaining the trust that makes banking relationships work.
What is third-party data?
Third-party data comes from external sources outside your bank and its partners. Market research firms, data analytics companies, and economic research organizations gather and sell this information to help banks understand broader market trends.
This data helps fill knowledge gaps about the wider financial landscape. For example, third-party data might reveal shifts in consumer spending patterns across different age groups or regions. It can show how customers interact with different financial services or highlight emerging banking preferences in specific demographics.
But third-party data needs careful evaluation before use. Since your bank didn’t collect this information directly, you must verify both its quality and compliance with privacy laws. Start by checking how providers collected their data and whether they had proper consent. Look for providers who clearly document their data sources and collection methods.
Quality varies significantly among third-party data providers. Some key questions to consider before purchasing:
- How recent is the data?
- How was it collected?
- What privacy protections are in place?
- How often is it updated?
- Which specific market segments does it cover?
Consider whether third-party data will truly add value beyond your existing information. Many banks find they can gain similar insights by analysing their first-party data more effectively. If you do use third-party data, document your reasons for using it and be transparent about your data sources.
Creating your banking data strategy
A clear data strategy helps your bank collect and use information effectively while protecting customer privacy. This matters most with first-party data – the information that comes directly from your customers’ banking activities.
Start by understanding what data you already have. Many banks collect valuable information through everyday transactions, website visits, and customer service interactions. Review these existing data sources before adding new ones. Often, you already have the insights you need – they just need better organization.
Map each type of data to a specific purpose. For example, transaction data might help detect fraud and improve service recommendations. Website analytics could reveal which banking features customers use most. Each data point should serve a clear business purpose while respecting customer privacy.
Strong data quality standards support better decisions. Create processes to update customer information regularly and remove outdated records. Check data accuracy often and maintain consistent formats across your systems. These practices help ensure your insights reflect reality.
Remember that strategy means choosing what not to do. You don’t need to collect every piece of data possible. Focus on information that helps you serve customers better while maintaining their privacy.
Managing multiple data sources
Banks work with many types of data – from direct customer interactions to market research. Each source serves a specific purpose, but combining them effectively requires careful planning and precise attention to regulations like GDPR and ePrivacy.
First-party data forms your foundation. It shows how your customers actually use your services and what they need from their bank. This direct interaction data proves most valuable because it reflects real behaviour rather than assumptions. When customers check their balances, transfer money, or apply for loans, they show you exactly how they use banking services.
Zero-party data adds context to these interactions. When customers share their financial goals or preferences directly, they help you understand the “why” behind their actions. This insight helps shape better services. For example, knowing a customer plans to buy a house helps you offer relevant savings tools or mortgage information at the right time.
Second-party partnerships can fill specific knowledge gaps. Working with trusted partners might reveal how customers manage their broader financial lives. But only pursue partnerships when they offer clear value to customers. Always explain these relationships clearly and protect shared information carefully.
Third-party data helps provide market context, but use it selectively. External market research can highlight broader trends or opportunities. However, this data often proves less reliable than information from direct customer interactions. Consider it a supplement to, not a replacement for, your own customer insights.
Keep these principles in mind when combining data sources:
- Prioritize direct customer interactions
- Focus on information that improves services
- Maintain consistent privacy standards across sources
- Document where each insight comes from
- Review regularly whether each source adds value
- Work with privacy and data experts to ensure customer information is handled properly
Enhance your web analytics strategy with Matomo
The financial sector finds powerful and compliant web analytics increasingly valuable as it navigates data management and privacy regulations. Matomo provides a configurable privacy-centric solution that meets the requirements of banks and financial institutions.
Matomo empowers your organisation to:
- Collect accurate, GDPR-compliant web data
- Integrate web analytics with your existing tools and platforms
- Maintain full control over your analytics data
- Gain insights without compromising user privacy
Matomo is trusted by some of the world’s biggest banks and financial institutions. Try Matomo for free for 30 days to see how privacy-focused analytics can get you the insights you need while maintaining compliance and user trust.
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Google Analytics Sampling : Why It Matters and How to Avoid It
21 octobre 2024, par Daniel Crough — Analytics TipsWhen marketing decisions rely on website analytics, accuracy matters.
However, Google Analytics and other analytics platforms sample data to
generate reports, which can sometimes misrepresent the true data trends.
