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• Open Banking Security 101 : Is open banking safe ?

  3 décembre 2024, par Daniel Crough — Banking and Financial Services

  Open banking is changing the financial industry. Statista reports that open banking transactions hit $57 billion worldwide in 2023 and will likely reach $330 billion by 2027. According to ACI, global real-time payment (RTP) transactions are expected to exceed $575 billion by 2028.

  Open banking is changing how banking works, but is it safe ? And what are the data privacy and security implications for global financial service providers ?

  This post explains the essentials of open banking security and addresses critical data protection and compliance questions. We’ll explore how a privacy-first approach to data analytics can help you meet regulatory requirements, build customer trust and ultimately thrive in the open banking market while offering innovative financial products.

   

  

  

  EU Financial Services Analytics Maturity Report

  Discover trends, strategies, and opportunities to balance compliance and competitiveness.

  Download the report now

  What is open banking ?

  Open banking is a system that connects banks, authorised third-party providers and technology, empowering customers to securely share their financial data with other companies. At the same time, it unlocks access to more innovative and personalised financial products and services like spend management solutions, tailored budgeting apps and more convenient payment gateways. 

  With open banking, consumers have greater choice and control over their financial data, ultimately fostering a more competitive financial industry, supporting technological innovation and paving the way for a more customer-centric financial future.

  Imagine offering your clients a service that analyses spending habits across all accounts — no matter the institution — and automatically finds ways to save them money. Envision providing personalised financial advice tailored to individual needs or enabling customers to apply for a mortgage with just a few taps on their phone. That’s the power of open banking.

  Embracing this technology is an opportunity for banks and fintech companies to build new solutions for customers who are eager for a more transparent and personalised digital experience.

  How is open banking different from traditional banking ?

  In traditional banking, consumers’ financial data is locked away and siloed within each bank’s systems, accessible only to the bank and the account holder. While account holders could manually aggregate and share this data, the process is cumbersome and prone to errors.

  With open banking, users can choose what data to share and with whom, allowing trusted third-party providers to access their financial information directly from the source. 

  

  How does open banking work ?

  The technology that makes open banking possible is the application programming interface (API). Think of banking APIs as digital translators for different software systems ; instead of translating languages, they translate data and code.

  The bank creates and publishes APIs that provide secure access to specific types of customer data, like credit card transaction history and account balances. The open banking API acts like a friendly librarian, ready to assist apps in accessing the information they need in a secure and organised way.

  Third-party providers, like fintech companies, use these APIs to build their applications and services. Some tech companies also act as intermediaries between fintechs and banks to simplify connections to multiple APIs simultaneously.

  For example, banks like BBVA (Spain) and Capital One (USA) offer secure API platforms. Fintechs like Plaid and TrueLayer use those banking APIs as a bridge to users’ financial data. This bridge gives other service providers like Venmo, Robinhood and Coinbase access to customer data, allowing them to offer new payment gateways and investment tools that traditional banks don’t provide.

  Is open banking safe for global financial services ?

  Yes, open banking is designed from the ground up to be safe for global financial services.

  Open banking doesn’t make customer financial data publicly available. Instead, it uses a secure, regulated framework for sharing information. This framework relies on strong security measures and regulatory oversight to protect user data and ensure responsible access by authorised third-party providers.

  In the following sections, we’ll explore the key security features and banking regulations that make this technology safe and reliable.

  Regulatory compliance in open banking

  Regulatory oversight is a cornerstone of open banking security.

  In the UK and the EU, strict regulations govern how companies access and use customer data. The revised Payment Services Directive (PSD2) in Europe mandates strong customer authentication and secure communication, promoting a high level of security for open banking services.

  To offer open banking services, companies must register with their respective regulatory bodies and comply with all applicable data protection laws.

  For example, third-party service providers in the UK must be authorised by the Financial Conduct Authority (FCA) and listed on the Financial Services Register. Depending on the service they provide, they must get an Account Information Service Provider (AISP) or a Payment Initiation Service Provider (PISP) license.

  Similar regulations and registries exist across Europe, enforced by the European National Competent Authority, like BaFin in Germany and the ACPR in France.

  In the United States, open banking providers don’t require a special federal license. However, this will soon change, as the U.S. Consumer Financial Protection Bureau (CFPB) unveiled a series of rules on 22 October 2024 to establish a regulatory framework for open banking.

  These regulations ensure that only trusted providers can participate in the open banking ecosystem. Anyone can check if a company is a trusted provider on public databases like the Regulated Providers registry on openbanking.org.uk. While being registered doesn’t guarantee fair play, it adds a layer of safety for consumers and banks.

  Key open banking security features that make it safe for global financial services

  Open banking is built on a foundation of solid security measures. Let’s explore five key features that make it safe and reliable for financial institutions and their customers.

  

  Strong Customer Authentication (SCA)

  Strong Customer Authentication (SCA) is a security principle that protects against unauthorised access to user financial data. It’s a regulated and legally required form of multi-factor authentication (MFA) within the European Economic Area.

  SCA mandates that users verify their identity using at least two of the following three factors :

  • Something they know (a password, PIN, security question, etc.)
  • Something they have (a mobile phone, a hardware token or a bank card)
  • Something they are (a fingerprint, facial recognition or voice recognition)

  This type of authentication helps reduce the risk of fraud and unauthorised transactions.

  API security

  PSD2 regulations mandate that banks provide open APIs, giving consumers the right to use any third-party service provider for their online banking services. According to McKinsey research, this has led to a surge in API adoption within the banking sector, with the largest banks allocating 14% of their IT budget to APIs. 

  To ensure API security, banks and financial service providers implement several measures, including :

  • API gateways, which act as a central point of control for all API traffic, enforcing security policies and preventing unauthorised access
  • API keys and tokens to authenticate and authorise API requests (the equivalent of a library card for apps)
  • Rate limiting to prevent denial-of-service attacks by limiting the number of requests a third-party application can make within a specific timeframe
  • Regular security audits and penetration testing to identify and address potential vulnerabilities in the API infrastructure

  Data minimisation and purpose limitation

  Data minimisation and purpose limitation are fundamental principles of data protection that contribute significantly to open banking safety.

  Data minimisation means third parties will collect and process only the data necessary to provide their service. Purpose limitation requires them to use the collected data only for its original purpose.

  For example, a budgeting app that helps users track their spending only needs access to transaction history and account balances. It doesn’t need access to the user’s full transaction details, investment portfolio or loan applications.

  Limiting the data collected from individual banks significantly reduces the risk of potential misuse or exposure in a data breach.

  Encryption

  Encryption is a security method that protects data in transit and at rest. It scrambles data into an unreadable format, making it useless to anyone without the decryption key.

  In open banking, encryption protects users’ data as it travels between the bank and the third-party provider’s systems via the API. It also protects data stored on the bank’s and the provider’s servers. Encryption ensures that even if a breach occurs, user data remains confidential.

  Explicit consent

  In open banking, before a third-party provider can access user data, it must first inform the user what data it will pull and why. The customer must then give their explicit consent to the third party collecting and processing that data.

  This transparency and control are essential for building trust and ensuring customers feel safe using third-party services.

  But beyond that, from the bank’s perspective, explicit customer consent is also vital for compliance with GDPR and other data protection regulations. It can also help limit the bank’s liability in case of a data breach.

  Explicit consent goes beyond sharing financial data. It’s also part of new data privacy regulations around tracking user behaviour online. This is where an ethical web analytics solution like Matomo can be invaluable. Matomo fully complies with some of the world’s strictest privacy regulations, like GDPR, lGPD and HIPAA. With Matomo, you get peace of mind knowing you can continue gathering valuable insights to improve your services and user experience while respecting user privacy and adhering to regulations.

  Risks of open banking for global financial services

  While open banking offers significant benefits, it’s crucial to acknowledge the associated risks. Understanding these risks allows financial institutions to implement safeguards and protect themselves and their customers.

  

  Risk of data breaches

  By its nature, open banking is like adding more doors and windows to your house. It’s convenient but also gives burglars more ways to break in.

  Open banking increases what cybersecurity professionals call the “attack surface,” or the number of potential points of vulnerability for hackers to steal financial data.

  Data breaches are a serious threat to banks and financial institutions. According to IBM’s 2024 Cost of a Data Breach Report, each breach costs companies in the US an average of $4.88 million. Therefore, banks and fintechs must prioritise strong security measures and data protection protocols to mitigate these risks.

  Risk of third-party access

  By definition, open banking involves granting third-party providers access to customer financial information. This introduces a level of risk outside the bank’s direct control.

  Financial institutions must carefully vet third-party providers, ensuring they meet stringent security standards and comply with all relevant data protection regulations.

  Risk of user account takeover

  Open banking can increase the risk of user account takeover if adequate security measures are not in place. For example, if a malicious third-party provider gains unauthorised access to a user’s bank login details, they could take control of the user’s account and make fraudulent bank transactions.

  A proactive approach to security, continuous monitoring and a commitment to evolving best practices and security protocols are crucial for navigating the open banking landscape.

  Open banking and data analytics : A balancing act for financial institutions

  The additional data exchanged through open banking unveils deeper insights into customer behaviour and preferences. This data can fuel innovation, enabling the development of personalised products and services and improved risk management strategies.

  However, using this data responsibly requires a careful balancing act.

  Too much reliance on data without proper safeguards can erode trust and invite regulatory issues. The opposite can stifle innovation and limit the technology’s potential.

  Matomo Analytics derisks web and app environments by giving full control over what data is tracked and how it is stored. The platform prioritises user data privacy and security while providing valuable data and analytics that will be familiar to anyone who has used Google Analytics.

  Open banking, data privacy and AI

  The future of open banking is entangled with emerging technologies like artificial intelligence (AI) and machine learning. These technologies significantly enhance open banking analytics, personalise services, and automate financial tasks.

  Several banks, credit unions and financial service providers are already exploring AI’s potential in open banking. For example, HSBC developed the AI-enabled FX Prompt in 2023 to improve forex trading. The bank processed 823 million client API calls, many of which were open banking.

  However, using AI in open banking raises important data privacy considerations. As the American Bar Association highlights, balancing personalisation with responsible AI use is crucial for open banking’s future. Financial institutions must ensure that AI-driven solutions are developed and implemented ethically, respecting customer privacy and data protection.

  Conclusion

  Open banking presents a significant opportunity for innovation and growth in the financial services industry. While it’s important to acknowledge the associated risks, security measures like explicit customer consent, encryption and regulatory frameworks make open banking a safe and reliable system for banks and their clients.

  Financial service providers must adopt a multifaceted approach to data privacy, implementing privacy-centred solutions across all aspects of their business, from open banking to online services and web analytics.

  By prioritising data privacy and security, financial institutions can build customer trust, unlock the full potential of open banking and thrive in today’s changing financial environment.

• Four Trends Shaping the Future of Analytics in Banking

  27 novembre 2024, par Daniel Crough — Banking and Financial Services

  While retail banking revenues have been growing in recent years, trends like rising financial crimes and capital required for generative AI and ML tech pose significant risks and increase operating costs across the financial industry, according to McKinsey’s State of Retail Banking report.

   

  Today’s financial institutions are focused on harnessing AI and advanced analytics to make their data work for them. To be up to the task, analytics solutions must allow banks to give consumers the convenient, personalised experiences they want while respecting their privacy.

   

  In this article, we’ll explore some of the big trends shaping the future of analytics in banking and finance. We’ll also look at how banks use data and technology to cut costs and personalise customer experiences.
  
  So, let’s get into it.

  

  This doesn’t just represent a security risk, it also impacts the usability for both customers and employees. Does any of the following sound familiar ?

  • Only specific senior employees know how to navigate the software to generate custom reports or use its more advanced features.
  • Customer complaints about your site’s usability or online banking experience are routine.
  • Onboarding employees takes much longer than necessary because of convoluted systems.
  • Teams and departments experience ‘data siloing,’ meaning that not everyone can access the data they need.

  These are warning signs that IT systems are ready for a review. Anyone thinking, “If it’s not broken, why fix it ?” should consider that legacy systems can also present data security risks. As more countries introduce regulations to protect customer privacy, staying ahead of the curve is increasingly important to avoid penalties and litigation.

  And regulations aren’t the only trends impacting the future of financial institutions’ IT and analytics.

  4 trends shaping the future of analytics in banking

  New regulations and new technology have changed the landscape of analytics in banking.

  New privacy regulations impact banks globally

  The first major international example was the advent of GDPR, which went into effect in the EU in 2018. But a lot has happened since. New privacy regulations and restrictions around AI continue to roll out.

  • The European Artificial Intelligence Act (EU AI Act), which was held up as the world’s first comprehensive legislation on AI, took effect on 31 July 2024.
  • In Europe’s federated data initiative, Gaia-X’s planned cloud infrastructure will provide for more secure, transparent, and trustworthy data storage and processing.
  • The revised Payment Services Directive (PSD2) makes payments more secure and strengthens protections for European businesses and consumers, aiming to create a more integrated and efficient payments market.

  But even businesses that don’t have customers in Europe aren’t safe. Consumer privacy is a hot-button issue globally.

  For example, the California Consumer Privacy Act (CCPA), which took effect in January, impacts the financial services industry more than any other. Case in point, 34% of CCPA-related cases filed in 2022 were related to the financial sector.

  California’s privacy regulations were the first in the US, but other states are following closely behind. On 1 July 2024, new privacy laws went into effect in Florida, Oregon, and Texas, giving people more control over their data.

  

  One typical issue for companies in the banking industry is that their privacy measures regarding user data collected from their website are much less lax than those in their online banking system.

  It’s better to proactively invest in a privacy-centric analytics platform before you get tangled up in a lawsuit and have to pay a fine (and are forced to change your system anyway). 

  And regulatory compliance isn’t the only bonus of an ethical analytics solution. The right alternative can unlock key customer insights that can help you improve the user experience.

  The demand for personalised banking services

  At the same time, consumers are expecting a more and more streamlined personal experience from financial institutions. 86% of bank employees say personalisation is a clear priority for the company. But 63% described resources as limited or only available after demonstrating clear business cases.

  McKinsey’s The data and analytics edge in corporate and commercial banking points out how advanced analytics are empowering frontline bank employees to give customers more personalised experiences at every stage :

  • Pre-meeting/meeting prep : Using advanced analytics to assess customer potential, recommend products, and identify prospects who are most likely to convert
  • Meetings/negotiation : Applying advanced models to support price negotiations, what-if scenarios and price multiple products simultaneously
  • Post-meeting/tracking : Using advanced models to identify behaviours that lead to high performance and improve forecast accuracy and sales execution

  Today’s banks must deliver the personalisation that drives customer satisfaction and engagement to outperform their competitors.

  The rise of AI and its role in banking

  With AI and machine learning technologies becoming more powerful and accessible, financial institutions around the world are already reaping the rewards.

  McKinsey estimates that AI in banking could add $200 to 340 billion annually across the global banking sector through productivity gains.

  • Credit card fraud prevention : Algorithms analyse usage to flag and block fraudulent transactions.
  • More accurate forecasting : AI-based tools can analyse a broader spectrum of data points and forecast more accurately.
  • Better risk assessment and modelling : More advanced analytics and predictive models help avoid extending credit to high-risk customers.
  • Predictive analytics : Help spot clients most likely to churn 
  • Gen-AI assistants : Instantly analyse customer profiles and apply predictive models to suggest the next best actions.

  Considering these market trends, let’s discuss how you can move your bank into the future.

  Using analytics to minimise risk and establish a competitive edge 

  With the right approach, you can leverage analytics and AI to help future-proof your bank against changing customer expectations, increased fraud, and new regulations.

  Use machine learning to prevent fraud

  Every year, more consumers are victims of credit and debit card fraud. Debit card skimming cases nearly doubled in the US in 2023. The last thing you want as a bank is to put your customer in a situation where a criminal has spent their money.

  This not only leads to a horrible customer experience but also creates a lot of internal work and additional costs.Thankfully, machine learning can help identify suspicious activity and stop transactions before they go through. For example, Mastercard’s fraud prevention model has improved fraud detection rates by 20–300%.

  

  Implementing a solution like this (or partnering with credit card companies who use it) may be a way to reduce risk and improve customer trust.

  Foresee and avoid future issues with AI-powered risk management

  Regardless of what type of financial products organisations offer, AI can be an enormous tool. Here are just a few ways in which it can mitigate financial risk in the future :

  • Predictive analytics can evaluate risk exposure and allow for more informed decisions about whether to approve commercial loan applications.
  • With better credit risk modelling, banks can avoid extending personal loans to customers most likely to default.
  • Investment banks (or individual traders or financial analysts) can use AI- and ML-based systems to monitor market and trading activity more effectively.

  Those are just a few examples that barely scratch the surface. Many other AI-based applications and analytics use cases exist across all industries and market segments.

  Protect customer privacy while still getting detailed analytics

  New regulations and increasing consumer privacy concerns don’t mean banks and financial institutions should forego website analytics altogether. Its insights into performance and customer behaviour are simply too valuable. And without customer interaction data, you’ll only know something’s wrong if someone complains.

  Fortunately, it doesn’t have to be one or the other. The right financial analytics solution can give you the data and insights needed without compromising privacy while complying with regulations like GDPR and CCPA.

  That way, you can track usage patterns and improve site performance and content quality based on accurate data — without compromising privacy. Reliable, precise analytics are crucial for any bank that’s serious about user experience.

  Use A/B testing and other tools to improve digital customer experiences

  Personalised digital experiences can be key differentiators in banking and finance when done well. But there’s stiff competition. In 2023, 40% of bank customers rated their bank’s online and mobile experience as excellent. 

  Improving digital experiences for users while respecting their privacy means going above and beyond a basic web analytics tool like Google Analytics. Invest in a platform with features like A/B tests and user session analysis for deeper insights into user behaviour.

  

  Behavioural analytics are crucial to understanding customer interactions. By identifying points of friction and drop-off points, you can make digital experiences smoother and more engaging.

  Matomo offers all this and is a great GDPR-compliant alternative to Google Analytics for banks and financial institutions. 

  Of course, this can be challenging. This is why taking an ethical and privacy-centric approach to analytics can be a key competitive edge for banks. Prioritising data security and privacy will attract other like-minded, ethically conscious consumers and boost customer loyalty.

  Get privacy-friendly web analytics suitable for banking & finance with Matomo

  Improving digital experiences for today’s customers requires a solid web analytics platform that prioritises data privacy and accurate analytics. And choosing the wrong one could even mean ending up in legal trouble or scrambling to reconstruct your entire analytics setup.

  Matomo provides privacy-friendly analytics with 100% data accuracy (no sampling), advanced privacy controls and the ability to run A/B tests and user session analysis within the same platform (limiting risk and minimising costs). 

  It’s easy to get started with Matomo. Users can access clear, easy-to-understand metrics and plenty of pre-made reports that deliver valuable insights from day one. Form usage reports can help banks and fintechs identify potential issues with broken links or technical glitches and reveal clues on improving UX in the short term.

  Over one million websites, including some of the world’s top banks and financial institutions, use Matomo for their analytics.

  Start your 21-day free trial to see why, or book a demo with one of our analytics experts.

• How HSBC and ING are transforming banking with AI

  9 novembre 2024, par Daniel Crough — Banking and Financial Services, Featured Banking Content

  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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  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 :

  1. Start with strong foundations
     • Invest in clear data governance frameworks
     • Set data quality standards
     • Build thorough documentation processes
     • Create transparent data tracking
  2. 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
  3. 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
  4. 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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