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• 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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  By providing your email and clicking “submit”, you agree to receive direct marketing materials relating to Matomo products and services, surveys, information about events, publications and promotions. You can unsubscribe at any time by clicking the opt-out link provided in each communication. We will process your personal information in accordance with our Privacy Policy.

  
  
  
  
  

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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

   

   

  Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.

  Watch the on-demand webinar : Advancing analytics maturity.

  Email

  By providing your email and clicking “submit”, you agree to receive direct marketing materials relating to Matomo products and services, surveys, information about events, publications and promotions. You can unsubscribe at any time by clicking the opt-out link provided in each communication. We will process your personal information in accordance with our Privacy Policy.

  
  
  
  
  

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• Piwik 1.12, New Features, API Improvements, Stability — The Last Piwik 1.X Release

  30 mai 2013, par Piwik team — Development

  We are very excited to announce the immediate availability of Piwik v1.12 !

  • Download Link
  • How to update Piwik ?
  • List of all tickets closed : Changelog

  Piwik v1.12 is a major new release with four big new features, seven smaller new features, several API improvements and all together 82 tickets fixed. This is also the last major 1.X release, which means after this release we will be working on releasing Piwik 2.0. This also means that you should upgrade to PHP 5.3 or higher if you haven’t already, since Piwik 2.0 will only support PHP 5.3 and above.

  Finally, this release contains two breaking changes to the API. If you use the Piwik API click here or scroll down to see if you’re affected.

  Table of Contents :

  1. New Big Feature – Beta Release Channel
  2. New Big Feature – Segment Editor
  3. New Big Feature – Page Speed Reports
  4. New Big Feature – Device Detection Reports
  5. Other Improvements
  6. API Changes and Improvements
  7. What we’re working on
  8. Participate in Piwik
  9. Summary

  New Big Feature – Beta Release Channel

  

  For those of you who want to help test Piwik 2.0-beta releases as soon as they come up, we’ve made it easier to use our beta releases. Navigate to the Settings > General Settings page and click the The latest beta release radio button. You will then be able to upgrade to beta releases.

  This isn’t truly a major feature, but we think it’s just as important because it will allow us to create more beta releases and thus catch more bugs before we make a final release. This means more releases and more stability for you.

  New Big Feature – Segment Editor

  

  The Segment Editor is a long-awaited new feature that allows you to view, save and edit your segments.

  Piwik has supported segmentation (filtering visits and reports by arbitrary criteria, like browser family) for quite some time now, but it has never been possible to visually create and modify them. Nor could they be saved for later recall.

  Thanks to the eighty individuals and company who funded this feature, it is now possible to :

  • visually segment your visitors, instead of creating URLs.
  • save segments and easily switch between them, instead of remembering URLs.
  • get suggestions for segments that might be helpful to view.
  • learn more in the Segmentating Analytics reports user documentation..

  New Big Feature – Page Speed Reports

  You can now see how long it took your webserver to generate and send pages over HTTP through the new Avg. Generation Time metric.

  This metric can be viewed on both the Pages and Page Titles reports :

  

  And the average page generation time for all the pages in your website/webapp is displayed on the visitors overview :

  

  You can use this new information to benchmark your webapp and web server.

  New Big Feature – Device Detection Reports

  Piwik 1.12 also includes a new plugin that provides reports on the device types (tablet, desktop, smartphone, etc.), device brands (Apple, Google, Samsung, etc.) and device models (iPad, Nexus 7, etc.) your visitors use to access your website :

  

  The new plugin also enhances Operating system detections (detecting sub versions of Linux, Windows, and more).

  Note : This plugin is not enabled by default, but will be in Piwik 2.0. If you want to view these reports now, you can activate the plugin in the Installed Plugins admin page. Navigate to Visitors > Devices to see the new reports. You may also use the new (beta) ‘Device type’.

  The new plugin was developed with the support of Clearcode.cc our technology partner

  

  Other improvements

  Majestic SEO Metrics

  

  We’ve added two new SEO metrics to the SEO widget, both of which are calculated by MajesticSEO.com. These metrics will tell you the number of external backlinks (the number of links to your site from other sites) and the number of referrer domains (the number of domains that link to your site).

  We thank the team at Majestic for their support and hard work in bringing you these metrics to your Piwik dashboards !

  Real-time Visitor Count Dashboard Widget

  

  There is now a simple new widget you can use to see the number of visitors, visits and actions that occurred in the last couple minutes. We call it the Real Time Visitor Counter !

  New segment parameter : siteSearchKeyword.

  There is now a new segment parameter you can use to segment your visits : siteSearchKeyword. This parameter will let you select visits that had site searches with a specific keyword.

  Ignore URL letter case when importing log files.

  We’ve added a new option to the log import script, –force-lowercase-path. When used, the importer will change URL paths to lowercase before tracking them. This way http://domain.com/MY/BLOG will be treated the same as http://domain.com/my/blog.

  Updated ISP Names

  

  We’ve also modified the Providers report so prettier and more up-to-date names of ISPs are displayed.

  Customize the background/text/axis color of graphs.

  

  It is now possible to change the background color, text color and/or axis color of the graph images generated by the ImageGraph plugin. To access this functionality, use the following URL query parameters when generating an image :

  • backgroundColor
  • textColor
  • axisColor

  For example :

  http://demo.piwik.org/index.php?module=API&method=ImageGraph.get&idSite=7&apiModule=UserSettings&apiAction=getBrowser&token_auth=anonymous&period=day&date=2013-03-21,2013-04-19&language=en&width=779&height=150&fontSize=9&showMetricTitle=0&aliasedGraph=1&legendAppendMetric=0&backgroundColor=efefef&gridColor=dcdcdc&colors=cb2026

  Send your users to a custom URL after they logout.

  If you manage a Piwik installation with many users and you want to send them to a custom page or website after they log out of Piwik, you can now specify the URL to redirect users after they log out.

  API Changes and Improvements

  BREAKING CHANGE – renamed segment parameters.

  The following segment parameters have been renamed :

  • continent renamed to : continentCode
  • browserName renamed to : browserCode
  • operatingSystem renamed to : operatingSystemCode
  • lat renamed to : latitude
  • long renamed to : longitude
  • region renamed to : regionCode
  • country renamed to : countryCode
  • continent renamed to : continentCode

  If you use one of the old segment parameter names, Piwik will throw an exception, so you should notice when you’re using an old name.

  BREAKING CHANGE – changes to the input & output of the Live.getLastVisitsDetails method.

  The following changes were made to the Live.getLastVisitsDetails API method :

  • The method no longer uses the maxIdVisit query parameter. It has been replaced by the filter_offset parameter.
  • Site search keywords are now displayed in a <siteSearchKeyword> element. They were formerly in <pageTitle> elements.
  • Custom variables with page scope now have ‘Page’ in their element names when displayed. For example, <customVariablePageName1>, <customVariablePageName2>, etc.

  Filter results of MultiSites.getAll by website name.

  It is now possible to filter the results of MultiSites.getAll by website name. To do this, set the pattern query parameter to the desired regex pattern.

  Get suggested values to use for a segment parameter.

  The new API method API.getSuggestedValuesForSegment can now be used to get suggested values for a segment parameter. This method will return a list of the most seen values (in the last 60 days) for a certain segment parameter. So for browserCode, this would return the codes for the browsers most visitors used in the last 60 days.

  Use extra tracking query parameters with the JS tracker (such as ‘lat’ & ‘long’).

  We’ve added a new method to the JavaScript tracker named appendToTrackingUrl. You can use this method to add extra query parameters to a tracking request, like so :

  _paq.push(['appendToTrackingUrl', 'lat=X&long=Y']);

  What we’re working on

  As we said above, Piwik v1.12 is the last in the 1.X series of releases. This means we are now officially working on Piwik 2.0.

  Piwik 2.0 will be a big release, to be sure, but it’s going to bring you more than just a couple new features and a bag of bug fixes. For Piwik 2.0 we will be revisiting the user needs and the ideals that originally prompted us to create Piwik in order to build our vision of the future of web analytics.

  Piwik 2.0 won’t just be a bigger, better web app, but a new platform for observing and analyzing the things that matter to you.

  Participate in Piwik

  Are you a talented developer or an experienced User Interface designer ? Or maybe you like to write documentation or are a marketing guru ?

  If you have some free time and if you want to contribute to one of the most awesome open source projects around, please get in touch with the Piwik team, or read this page to learn more…

  Summary

  For the full list of changes in Piwik 1.12 check out the Changelog.

  Thank you to the core developers, all the beta testers and users, our official supporters, the translators & everyone who reported bugs or feature requests. Also thank you to softwares we use, and the libraries we use.

  If you are a company and would like to help an important project like Piwik grow, please get in touch, it means a lot to us. You can also participate in the project —

  –> if you like what you read, please tell your friends and colleagues or write on your website, blog, forums, stackoverflow, etc. <–
  

  Peace. Enjoy !

• What is data anonymization in web analytics ?

  11 février 2020, par Joselyn Khor — Analytics Tips, Privacy
  Collecting information via web analytics platforms is needed to help a website grow and improve. When doing so, it’s best to strike a balance between getting valuable insights, and keeping the trust of your users by protecting their privacy.

  This means not collecting or processing any personally identifiable information (PII). But what if your organisation requires you to collect PII ?

  That’s where data anonymization comes in.

  

  What is data anonymization ?

  Data anonymization makes identifiable information unidentifiable. This is done through data processing techniques which remove or modify PII data. So data becomes anonymous and can’t be linked to any individual.

  In the context of web analytics, data anonymization is handy because you can collect useful data while protecting the privacy of website visitors.

  Why is data anonymization important ?

  Through modern threats of identity theft, credit card fraud and the like, data anonymization is a way to protect the identity and privacy of individuals. As well as protect private and sensitive information of organisations. 

  Data anonymization lets you follow the many laws around the world which protect user privacy. These laws provide safeguards around collecting personal data or personally identifiable information (PII), so data anonymization is a good solution to ensure you’re not processing such sensitive information.

  In some cases, implementing data anonymization techniques means you can avoid having to show your users a consent screen. Which means you may not need to ask for consent in order to track data. This is a bonus as consent screens can annoy and stop people from engaging with your site.

  GDPR and data anonymization

  

  The GDPR is a law in the EU that limits the collection and processing of personal data. The aim is to give people more control over their online personal information. Which is why website owners need to follow certain rules to become GDPR compliant and protect user privacy. According to the GDPR, you can be fined up to 4% of your yearly revenue for data breaches or non-compliance. 

  In the case of web analytics, tools can be easily made compliant by following a number of steps. 

  This is why anonymizing data is a big deal.

  Anonymized data isn’t personal data according to the GDPR : 

  “The principles of data protection should therefore not apply to anonymous information, namely information which does not relate to an identified or identifiable natural person or to personal data rendered anonymous in such a manner that the data subject is not or no longer identifiable.”

  This means, you still get the best of both worlds. By anonymizing data, you’re still able to collect useful information like visitor behavioural data.

  US privacy laws and data anonymization

  In the US, there isn’t one single law that governs the protection of personal data, called personally identifiable information (PII). There are hundreds of federal and state laws that protect the personal data of US residents. As well as, industry-specific statutes related to data privacy, like the California Consumer Privacy Act (CCPA) and the Health Insurance Portability and Accountability Act (HIPAA).

  Website owners in the US need to know exactly what laws govern their area of business in order to follow them.

  A general guideline is to protect user privacy regardless of whether you are or aren’t allowed to collect PII. This means anonymizing identifiable information so your website users aren’t put at risk.

  Data anonymization techniques in Matomo Analytics

  If you carry these out, you won’t need to ask your website visitors for tracking consent since anonymized data is no longer considered personal data under the GDPR.

  • Anonymize previously tracked raw data – like visitors/visits
  • Anonymize Visitor IP addresses
  • Anonymize geo-location information (country, region, city)
  • Pseudonymize User ID ( if you don’t want this data to land on your Matomo server, the solution is simply to not set the User ID. Then Matomo will use the normal anonymized IDs)
  • Data can also be anonymized before it’s even sent to Matomo
  • Learn how to further configure privacy settings in a web analytics tool like Matomo
  • 11 ways Matomo Analytics helps you to protect your visitors privacy

  The techniques listed above make it easy for you when using a tool like Matomo, as they are automatically anonymized.

  Tools like Google Analytics on the other hand don’t provide some of the privacy options and leave it up to you to take on the burden of implementation without providing steps.

  Data anonymization tools

  If you’re a website owner who wants to grow your business or learn more about your website visitors, privacy-friendly tools like Matomo Analytics are a great option. By following the easy steps to be GDPR compliant, you can anonymize all data that could put your visitors at risk.