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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.

  
  
  
  
  

  Δ

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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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• Of ctors and dtors

  18 février 2011, par Multimedia Mike — Programming, Sega Dreamcast

  I haven’t given up on the Sega Dreamcast programming. I was able to compile a bunch of homebrew code for the DC many years ago and I can’t make it work anymore. Again, I was working with a purpose-built, open source RTOS named KallistiOS (or KOS). I can make the programs compile but not run. I had ELF files left over from years ago which still executed. But when I tried to build new ELF files, no luck— the programs crashed before even reaching my main() function.

  I found the problem : ELF files are comprised of a number of sections and 2 of these sections are named ’.ctors’ and ’.dtors’ which stand for constructors and destructors. The KOS RTOS performs a manual traversal of .ctors section during program initialization and this is where things go bad. The traversal code doesn’t seem to account for a .ctors section that only contains a single entry. I commented out the function that does the traversal and programs started to work, at least until it was time to exit the program and return control to the program loader. That’s when the counterpart .dtors section traversal code ran and demonstrated the same problem. I’ll exhibit the problematic code at the end of this post.

  So I’m finally tinkering with Sega Dreamcast programming once again and with a slightly better grasp of software engineering than the first time I did this.

  Portable and Compatible C ?
  If nothing else, this low-level embedded stuff exposes you to some serious toolchain arcana, the likes of which you will likely never see working strictly in the desktop arena.

  Still, this exercise makes me wonder why C code from a decade ago doesn’t compile reliably now. Part of it is because gcc has gotten stricter about the syntax it will accept. In the case of this specific crashing problem, I suspect it comes down to a difference in the way the linker generates the final ELF file. I’ve written a list of items I have had to modify in the KOS codebase in order to get it to compile on more recent gcc versions. I wonder if it would be worth publishing the specifics, or if anyone would ever find the information useful ? Oh, who am I kidding ? Of course I’ll write it up, perhaps publish a new version of the code, if only because that’s the best chance I have of finding my own work again some years down the road.

  Problematic C Code
  See if this code makes any sense to you. It somehow traverse a list of 32-bit function pointers (in different directions, depending on constructors or destructors), executing each in turn. However, it appears to fall over if the list of pointers consists of a single entry.
  

  PLAIN TEXT

  C :

  1. typedef void (*fptr)(void) ;
  2.  
  3. static fptr ctor_list[1] __attribute__((section(".ctors"))) = { (fptr) -1 } ;
  4. static fptr dtor_list[1] __attribute__((section(".dtors"))) = { (fptr) -1 } ;
  5.  
  6. /* Call this to execute all ctors */
  7. void arch_ctors() {
  8.     fptr *fpp ;
  9.  
  10.     /* Run up to the end of the list (defined by crtend) */
  11.     for (fpp=ctor_list + 1 ; *fpp != 0 ; ++fpp)
  12.          ;
  13.  
  14.     /* Now run the ctors backwards */
  15.     while (—fpp> ctor_list)
  16.         (**fpp)() ;
  17. }
  18.  
  19. /* Call this to execute all dtors */
  20. void arch_dtors() {
  21.     fptr *fpp ;
  22.  
  23.     /* Do the dtors forwards */
  24.     for (fpp=dtor_list + 1 ; *fpp != 0 ; ++fpp )
  25.         (**fpp)() ;
  26. }

• MPEG-DASH - Multiplexed Representations Issue

  26 avril 2017, par Mike

  I’m trying to learn ffmpeg, MP4Box, and MPEG-DASH, but I’m running into an issue with the .mp4 I’m using. I’m using ffmpeg to demux the mp4 with this command :

  ffmpeg -i test.mp4 -c:v copy -g 72 -an video.mp4 -c:a copy audio.mp4

  Once the two files are created, I use MP4Box to segment the files for the dash player using this command :

  MP4Box -dash 4000 -frag 1000 -rap -segment-name segment_ output.mp4

  Which does create all the files I think I need. Then I point the player to the output_dash.mpd and nothing happens except a ton of messages in the console :

  [8] EME detected on this user agent! (ProtectionModel_21Jan2015) 
  
  [11] Playback Initialized 
  
  [21] [dash.js 2.3.0] MediaPlayer has been initialized 
  
  [64] Parsing complete: ( xml2json: 3.42ms, objectiron: 2.61ms, total: 0.00603s) 
  
  [65] Manifest has been refreshed at Wed Apr 12 2017 12:16:52 GMT-0600 (MDT)[1492021012.196]  
  
  [72] MediaSource attached to element.  Waiting on open... 
  
  [77] MediaSource is open! 
  
  [77] Duration successfully set to: 148.34 
  
  [78] Added 0 inline events 
  
  [78] No video data. 
  
  [79] No audio data. 
  
  [79] No text data. 
  
  [79] No fragmentedText data. 
  
  [79] No embeddedText data. 
  
  [80] Multiplexed representations are intentionally not supported, as they are not compliant with the DASH-AVC/264 guidelines 
  
  [81] No streams to play.

  Here is the MP4Box -info on the video I’m using :

  * Movie Info *
  
      Timescale 1000 - Duration 00:02:28.336
  
      Fragmented File no - 2 track(s)
  
      File suitable for progressive download (moov before mdat)
  
      File Brand mp42 - version 512
  
      Created: GMT Wed Feb  6 06:28:16 2036
  
  
  
  File has root IOD (9 bytes)
  
  Scene PL 0xff - Graphics PL 0xff - OD PL 0xff
  
  Visual PL: Not part of MPEG-4 Visual profiles (0xfe)
  
  Audio PL: Not part of MPEG-4 audio profiles (0xfe)
  
  No streams included in root OD
  
  
  
  iTunes Info:
  
      Name: Rogue One - A Star Wars Story
  
      Artist: Lucasfilm
  
      Genre: Trailer
  
      Created: 2016
  
      Encoder Software: HandBrake 0.10.2 2015060900
  
      Cover Art: JPEG File
  
  
  
  Track # 1 Info - TrackID 1 - TimeScale 90000 - Duration 00:02:28.335
  
  Media Info: Language "Undetermined" - Type "vide:avc1" - 3552 samples
  
  Visual Track layout: x=0 y=0 width=1920 height=816
  
  MPEG-4 Config: Visual Stream - ObjectTypeIndication 0x21
  
  AVC/H264 Video - Visual Size 1920 x 816
  
      AVC Info: 1 SPS - 1 PPS - Profile High @ Level 4.1
  
      NAL Unit length bits: 32
  
      Pixel Aspect Ratio 1:1 - Indicated track size 1920 x 816
  
  Self-synchronized
  
  
  
  Track # 2 Info - TrackID 2 - TimeScale 44100 - Duration 00:02:28.305
  
  Media Info: Language "English" - Type "soun:mp4a" - 6387 samples
  
  MPEG-4 Config: Audio Stream - ObjectTypeIndication 0x40
  
  MPEG-4 Audio MPEG-4 Audio AAC LC - 2 Channel(s) - SampleRate 44100
  
  Synchronized on stream 1
  
  Alternate Group ID 1

  I know I need to separate the video and audio and I think that’s where my issue is. The command I’m using probably isn’t doing the right thing.

  Is there a better command to demux my mp4 ?
  Is the MP4Box command I’m using best for segmenting the files ?
  If I use different files, will they always need to be demuxed ?

  One thing to mention, if I use the following commands everything works fine, but there is no audio because of the -an which means it’s only video :

  ffmpeg -i test.mp4 -c:v copy -g 72 -an output.mp4
  
  
  
  MP4Box -dash 4000 -frag 1000 -rap -segment-name segment_ output.mp4

  UPDATE

  I noticed that the video had no audio stream, but the audio had the video stream which is why I got the mux error. I thought that might be an issue so I ran this command to keep the unwanted streams out of the outputs :

  ffmpeg -i test.mp4 -c:v copy -g 72 -an video.mp4 -c:a copy -vn audio.mp4

  then I run :

  MP4Box -dash 4000 -frag 1000 -rap -segment-name segment_ video.mp4 audio.mp4

  now I no longer get the Multiplexed representations are intentionally not supported... message, but now I get :

  [122] Video Element Error: MEDIA_ERR_SRC_NOT_SUPPORTED 
  
  [123] [object MediaError] 
  
  [125] Schedule controller stopping for audio 
  
  [126] Caught pending play exception - continuing (NotSupportedError: Failed to load because no supported source was found.)

  I tried playing the video and audio independently through Chrome and they both work, just not through the dash player. Ugh, this is painful to learn, but I feel like I’m making progress.