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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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  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.

  
  
  
  
  

  Δ

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• Architecture of video-based service for mobile phones

  27 juin 2015, par David Azar

  I guess this is more of a conceptual question than a technical one.

  I’m trying to figure out the best way to upload short videos to a server and also be able to download them and watch them on both Android and iOS.

  Lets focus on Android for the moment.

  I’ve done some experiments, and my results have been :

  • I’m able to compress 12-14MB video down to 500KB using FFMPEG lib with pretty good results in quality, but it takes about 12 seconds.

  • Next, im uploading those videos to my Parse backend as ParseFile to store them.

  • Finally, i can download them and watch them with no problem using a VideoView widget.

  Now, for the tests i’ve been running, these are great results. But i want to see if there is a better way to manage and scale all of this.

  My questions are :

  • Is there a better, lighter way to compress video ?

  • Is Parse the right way to go ?

  • How can i stream videos instead of downloading them and storing the on local storage before playing them ? i know this will cause my app to use significant space on disk and i dont want that.

  • How do big companies do this kind of tasks ?

  I’ve heard Amazon S3 is a cool thing for projects like this one, also Google Cloud Platform. I want to understand the best approach before building everything so i can do it the right way and also, provide the absolute best user experience for watching these videos.

• Downloading youtube mp3 - metadata encoding issue (python, youtube-dl, ffmpeg)

  21 mai 2015, par mopsiok

  I’m trying to download audio from youtube with youtube-dl.exe and ffmpeg.exe (Windows 7), but I am having some troubles with encoding. I have to parse metadata manually, because when I try to use

  --metadata-from-title "%(artist) - %(title)" --extract-audio --audio-format mp3 https://www.youtube.com/watch?v=DaU94Ld3fuM

  I get ERROR : Could not interpret title of video as "%(artist) - %(title)"

  Anyway, I wrote some code to save metadata with ffmpeg :

  def download(url, title_first=False):
  
      if (0 == subprocess.call('youtube-dl --extract-audio --audio-format mp3 %s' % url)):
  
          #saves file in current directory in format: VID_TITLE-VID_ID.mp3
  
          video_id = url[url.find('=')+1:] #video id from URL (after ?v=)
  
          for f in os.listdir('.'):
  
              if video_id in f:
  
                  filename = f
  
                  break
  
          os.rename(filename, video_id+'.mp3') #name without non-ascii chars (for tests)
  
          video_title = filename[: filename.find(video_id)-1]
  
  
  
          output = video_title + '.mp3'
  
          title, artist = '', ''
  
          try: #parsing the title
  
              x = video_title.find('-')
  
              artist = video_title[:x].strip()
  
              title = video_title[x+1:].strip()
  
              if (title_first): output = '%s - %s.mp3' % (title, artist)
  
          except:
  
              pass
  
  
  
          x = 'ffmpeg -i "%s" -metadata title="%s" -metadata artist="%s" -acodec copy -id3v2_version 3 -write_id3v1 1 "%s"' \
  
                          % (video_id+'.mp3', title, artist, output)
  
          print x
  
          subprocess.call(x)

  The file is downloaded and then cropped to given start and duration times (the code above is a simplified version). Filename is fine, but when I open the file with AIMP3, it shows rubbish instead of non-ascii characters :

  

  I’ve tried to re-encode the final command with iso-8859-2, utf-8 and mbcs :

  x = x.decode('cp1250').encode('iso-8859-2')

  But non-ascii chars are still not readable. Passing an unicode command returns UnicodeEncodeError...

  Any idea how to solve this problem ?