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• Strategies for Reducing Bank Customer Acquisition Cost [2024]

  24 septembre 2024, par Daniel Crough — Banking and Financial Services

  Acquiring new customers is no small feat — regardless of the size of your team. The expenses of various marketing efforts tend to pile up fast, even more so when your business operates in a highly competitive industry like banking. At the same time, marketing budgets continue to decrease — dropping from an average of 9.1% of total company revenue in 2023 down to 7.7% in 2024 — prompting businesses in the financial services industry to figure out how they can do more with less.

  That brings us to bank customer acquisition cost (CAC) — a key business metric that can reveal quite a bit about your bank’s long-term profitability and potential for achieving sustainable growth. 

  This article will cover the ins and outs of bank customer acquisition costs and share actionable tips and strategies you can implement to reduce CAC.

  What is customer acquisition cost in banking ? 

  

  The global market volume of neobanks — fintech companies and digital banking platforms, often referred to as “challenger banks” — was estimated at $4.96 trillion in 2023. It’s expected to continue growing at a compound annual growth rate (CAGR) of 13.15% in the coming years, potentially reaching $10.44 trillion by 2028.

  That’s enough of an indicator that the financial services industry is now a highly competitive landscape where companies are often competing for the attention of a relatively limited audience. 

  Plus, several app-only banks based in Europe have made significant progress in attracting new customers to their financial products : 

  

  Unsurprisingly, this flurry of competition is putting upward pressure on customer acquisition and retention costs across the banking sector.

  Customer acquisition cost (CAC) — the sum of all costs and resources related to acquiring an additional customer — is one of the key business metrics to keep an eye on when trying to maximise your return on investment (ROI) and profitability, especially if your company operates in the banking industry.

  Here’s the basic formula you can use to calculate the cost of acquisition in banking : 

  Customer Acquisition Cost (CAC) = Total Amount Spent (TS) / Total New Customers Acquired (TNC)

  In essence, it requires you to divide the total cost of acquiring consumers — including sales and marketing expenses — by the total number of new customers your company has gained within a specific timeframe.

  There’s one thing you need to keep in mind : 

  The customer acquisition process involves more than just your marketing and sales departments. 

  While marketing and sales channels play a crucial role in this process, the list of expenses that may contribute to customer acquisition costs in banking goes well beyond that. 

  Here’s a quick breakdown of the customer acquisition cost formula to show you which costs make up the total amount spent : 

  • All advertising and marketing costs, including traditional (direct mail, billboards, TV and print advertising) and digital channels (email, Google ads, social media and influencer marketing)
  • Cost of outsourced marketing services, including any independent contractors involved in the process 
  • Salaries and commissions for the marketing team and sales representatives
  • Software subscriptions, including marketing software and web analytics tools 
  • Other overhead and operational costs 

  And until you’ve taken all these expenses into account, you won’t be able to accurately estimate how much it actually costs you to attract potential customers.

  Another thing to keep in mind is that there’s no universal definition of “good CAC.” 

  The average customer acquisition cost varies across different industries and business models. That said, you can generally expect a higher-than-average CAC in highly competitive sectors — namely, the financial, manufacturing and real estate industries. 

  Importance of tracking customer acquisition cost in banking 

  

  Customer acquisition costs are an important indicator of a banking business’s potential growth and profitability. Monitoring this fundamental business metric can provide data-driven insights about your current bank customer acquisition strategy — and offers a few notable benefits : 

  • Measuring the performance and effectiveness of different channels and campaigns and making data-driven decisions regarding future marketing efforts
  • Improving return on investment (ROI) by determining the most effective strategies for acquiring new customers 
  • Improving profitability by assessing the value per customer and improving profit margins 
  • Benchmarking against industry competitors to see where your business’s CAC stands compared to the banking industry average

  At the risk of stating the obvious, acquiring new customers isn’t always easy. That’s true for many highly competitive industries — especially the banking sector, which is currently witnessing the rapid rise of digital disruptors. 

  Case in point, the fintech market alone is currently valued at $312.98 billion and is expected to reach $556.70 billion by 2030, following a CAGR of 14%.

  

  However, strong competition is only one of the challenges banks face throughout the process of attracting potential customers. 

  Here are a few other things to keep in mind : 

  • Ethical business practices and strict compliance requirements when it comes to the privacy and security of customer data, including meeting data protection standards and ensuring regulatory compliance
  • Lack of personalisation throughout the customer journey, which today’s customers view as a lack of understanding of — and even interest in — their needs and preferences 
  • Limited mobile banking capabilities, which further points to a failure to innovate and adapt — one of the leading risks that financial services may face 

  7 strategies for reducing bank customer acquisition costs 

  

  When working on optimising your banking customer acquisition strategy, the key thing to keep in mind is that there are two sides to improving CAC : 

  On the one hand, you have efforts to decrease the costs associated with acquiring a new customer — and on the other, you have the importance of attracting high-value customers. 

  1. Eliminate friction points in the customer onboarding process

  One of the first things financial institutions should do is examine their existing digital onboarding process and look for friction points that might cause potential customers to drop off. After all, a streamlined onboarding process will minimise barriers to conversion, increasing the number of new customers acquired and improving overall customer satisfaction. 

  Keep in mind that, at the 30-day mark, finance mobile apps have an average user retention rate of 3% : 

  

  That says a lot about the importance of providing a frictionless onboarding experience as a retail bank or any other financial institution. 

  Granted, a single point of friction is rarely enough to cause customers to churn. It’s typically a combination of several factors — a lengthy sign-up process with complicated password requirements and time-consuming customer identification or poor customer service, for example — that occur during the key moments of the customer journey.

  In order to keep tabs on customer experiences across different touchpoints and spot potential barriers in their journey, you’ll need a reliable source of data. Matomo’s Funnels report can show you exactly where your website visitors are dropping off. 

  2. Get more personalised with your marketing efforts 

  Generic experiences are rarely the way to go — especially when you’re contending for the attention of prospective customers in such a competitive sector. 

  Besides, 62% of people who made an online purchase within the last six months have said that brands would lose their loyalty following a non-personalised experience. 

  What’s more shocking is that only a year earlier, that number stood at 45%.

  

  When it comes to improving marketing efficiency and sales strategies, 94% of marketers agree that personalisation is key : 

  

  It’s evident that personalised marketing supported by behavioural segmentation can significantly improve conversion rates — and, most importantly, reduce acquisition costs. 

  Of course, it’s virtually impossible to deliver targeted, personalised marketing messaging without creating audience segments and detailed buyer personas. Matomo’s Segmentation feature can help by allowing you to split website visitors into smaller groups and get much-needed insights for behavioural segmentation. 

  3. Build an omnichannel marketing strategy 

  Customer expectations, behaviours and preferences are constantly evolving, making it crucial for financial services to adapt their customer acquisition strategies accordingly. Meeting prospective customers on their preferred channels is a big part of that. 

  The issue is that modern banking customers tend to move across different channels. That’s one of the reasons why it’s becoming increasingly more difficult to deliver a unified experience throughout the entire customer journey and close the gap between digital and in-person customer interactions. 

  Omnichannel marketing gives you a way to keep up with customers’ ever-evolving expectations :

  Adopting this marketing strategy will allow you to meet customers where they are and deliver a seamless experience across a wide range of digital channels and touchpoints, leading to more exposure — and, ultimately, increasing the number of acquired customers.

  Matomo can support your omnichannel efforts by providing accurate, unsampled data needed for cross-channel analytics and marketing attribution. 

  4. Work on your social media presence 

  Social networks are among the most popular — and successful — digital marketing channels, with millions (even billions, depending on the platform) of active users. 

  In fact, 89% of marketers report using Facebook as their main platform for social media marketing, while another 80% use Instagram to reach their target audience and promote their business. 

  

  And according to The State of Social Media in Banking 2023 report, nine out of ten banks (89%) consider social media is important, while another 88% are active on their social media accounts. 

  That is to say, even traditionally conservative industries — like banking and finance — realise the crucial role of social media in promoting their services and engaging with customers on their preferred channels : 

  It’s an excellent way for businesses in the financial sector to gain exposure, drive traffic to their website and acquire new customers. 

  If you’re ready to improve social media visibility as part of your multichannel efforts, Matomo can help you track social media activity across 70 different platforms. 

  5. Shift the focus on customer loyalty and retention 

  Up until this point, the focus has mainly been on building new business relationships. However, one thing to keep in mind is that retaining existing customers is generally cheaper than investing in customer acquisition activities to attract new ones. 

  Of course, customer retention won’t directly impact your CAC. But what it can do is increase customer lifetime value, contributing to your company’s revenue and profits — which, in turn, can “balance out” your acquisition costs in the long run.

  That’s not to say that you should stop trying to bring in new clients ; far from it. 

  However, focusing on increasing customer loyalty — namely, delivering excellent customer service and building lasting business relationships — could motivate satisfied customers to become brand advocates. 

  As this survey of customer satisfaction for leading banks in the UK has shown, when clients are satisfied with a bank’s products and services, they’re more likely to recommend it. 

  

  Positive word-of-mouth recommendations can be a powerful way to drive customer acquisition. You can leverage that by launching a customer referral program and incentivising loyal customers to refer new ones to your business. 

  6. A/B test different elements to find ones that work 

  We’ve already underlined the importance of understanding your audience ; it’s the foundation for optimising the customer journey and delivering targeted marketing efforts that will attract more customers. 

  Another proven method that can be used to refine your customer acquisition strategy is A/B or split testing. 

  It involves testing different versions of specific elements of your marketing content — such as language, CTAs and visuals — to determine the most effective combinations that resonate with your target audience. 

  Besides your marketing campaigns, you can also split test different variants of your website or mobile app to see which version gets them to convert. 

  Matomo’s A/B Testing feature can be of huge help here : 

  

  7. Track other relevant customer acquisition metrics 

  To better assess your company’s profitability, you’ll have to go beyond CAC and factor in other critical metrics — namely, customer lifetime value (CLTV), churn rate and return on investment (ROI). 

  Here are the most important KPIs you should monitor in addition to CAC : 

  • Customer lifetime value (CLTV), which represents the revenue generated by a single customer throughout the duration of their relationship with your company and is another crucial indicator of customer profitability 
  • Churn rate — the rate at which your company loses clients within a given timeframe — can indicate how well you’re retaining customers 
  • Return on investment (ROI) — the revenue generated by new clients compared to the initial costs of acquiring them — can help you identify the most effective customer acquisition channels 

  These metrics work hand in hand. There needs to be a balance between the revenue the customer generates over their lifetime and the costs related to attracting them.

  Ideally, you should be aiming for lower CAC and customer churn and higher CLTV ; that’s usually a solid indicator of financial health and sustainable growth. 

  Lower bank customer acquisition costs with Matomo 

  Acquiring new customers will require a lot of time and resources, regardless of the industry you’re working in — but can be even more challenging in the financial sector, where you have to adapt to the ever-changing customer expectations and demands. 

  The strategies outlined above — combined with a thorough understanding of your customer’s behaviours and preferences — can help you lower the cost of bank customer acquisition.

  On that note, you can learn a lot about your customers through web analytics — and use those insights to support your customer acquisition process and ensure you’re delivering a seamless online banking experience. 

  If you need an alternative to Google Analytics that doesn’t rely on data sampling and ensures compliance with the strictest privacy regulations, all while being easy to use, choose Matomo — the go-to web analytics platform for more than 1 million websites around the globe. 

  CTA : Start your 21-day free trial today to see how Matomo’s all-in-one solution can help you understand and attract new customers — all while respecting their privacy. 

• Banking Data Strategies – A Primer to Zero-party, First-party, Second-party and Third-party data

  25 octobre 2024, par Daniel Crough — Banking and Financial Services, Privacy

  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.

• 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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