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• ffmpeg piped output producing incorrect metadata frame count with Python

  6 décembre 2024, par Xorgon

  Using Python, I am attempting to use ffmpeg to compress videos and put them in a PowerPoint. This works great, however, the video files themselves have incorrect frame counts which can cause issues when I read from those videos in other code.

  


  Edit for clarification : by "frame count" I mean the metadata frame count. The actual number of frames contained in the video is correct, but querying the metadata gives an incorrect frame count.

  


  Having eliminated the PowerPoint aspect of the code, I've narrowed this down to the following minimal reproducing example of saving an output from an ffmpeg pipe :

  


  from subprocess import Popen, PIPE

video_path = 'test_mp4.mp4'

ffmpeg_pipe = Popen(['ffmpeg',
                     '-y',  # Overwrite files
                     '-i', f'{video_path}',  # Input from file
                     '-f', 'avi',  # Output format
                     '-c:v', 'libx264',  # Codec
                     '-'],  # Output to pipe
                    stdout=PIPE)

new_path = "piped_video.avi"
vid_file = open(new_path, "wb")
vid_file.write(ffmpeg_pipe.stdout.read())
vid_file.close()


  


  I've tested several different videos. One small example video that I've tested can be found here.

  


  I've tried a few different codecs with avi format and tried libvpx with webm format. For the avi outputs, the frame count usually reads as 1073741824 (2^30). Weirdly, for the webm format, the frame count read as -276701161105643264.

  


  This is a snippet I used to read the frame count, but one could also see the error by opening the video details in Windows Explorer and seeing the total time as something like 9942 hours, 3 minutes, and 14 seconds.

  


  import cv2

video_path = 'test_mp4.mp4'
new_path = "piped_video.webm"

cap = cv2.VideoCapture(video_path)
print(f"Original video frame count: = {int(cap.get(cv2.CAP_PROP_FRAME_COUNT)):d}")
cap.release()

cap = cv2.VideoCapture(new_path)
print(f"Piped video frame count: = {int(cap.get(cv2.CAP_PROP_FRAME_COUNT)):d}")
cap.release()


  


  For completeness, here is the ffmpeg output :

  


  ffmpeg version 2023-06-11-git-09621fd7d9-full_build-www.gyan.dev Copyright (c) 2000-2023 the FFmpeg developers
  built with gcc 12.2.0 (Rev10, Built by MSYS2 project)
  configuration: --enable-gpl --enable-version3 --enable-static --disable-w32threads --disable-autodetect --enable-fontconfig --enable-iconv --enable-gnutls --enable-libxml2 --enable-gmp --enable-bzlib --enable-lzma --enable-libsnappy --enable-zlib --enable-librist --enable-libsrt --enable-libssh --enable-libzmq --enable-avisynth --enable-libbluray --enable-libcaca --enable-sdl2 --enable-libaribb24 --enable-libaribcaption --enable-libdav1d --enable-libdavs2 --enable-libuavs3d --enable-libzvbi --enable-librav1e --enable-libsvtav1 --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libaom --enable-libjxl --enable-libopenjpeg --enable-libvpx --enable-mediafoundation --enable-libass --enable-frei0r --enable-libfreetype --enable-libfribidi --enable-liblensfun --enable-libvidstab --enable-libvmaf --enable-libzimg --enable-amf --enable-cuda-llvm --enable-cuvid --enable-ffnvcodec --enable-nvdec --enable-nvenc --enable-d3d11va --enable-dxva2 --enable-libvpl --enable-libshaderc --enable-vulkan --enable-libplacebo --enable-opencl --enable-libcdio --enable-libgme --enable-libmodplug --enable-libopenmpt --enable-libopencore-amrwb --enable-libmp3lame --enable-libshine --enable-libtheora --enable-libtwolame --enable-libvo-amrwbenc --enable-libcodec2 --enable-libilbc --enable-libgsm --enable-libopencore-amrnb --enable-libopus --enable-libspeex --enable-libvorbis --enable-ladspa --enable-libbs2b --enable-libflite --enable-libmysofa --enable-librubberband --enable-libsoxr --enable-chromaprint
  libavutil      58. 13.100 / 58. 13.100
  libavcodec     60. 17.100 / 60. 17.100
  libavformat    60.  6.100 / 60.  6.100
  libavdevice    60.  2.100 / 60.  2.100
  libavfilter     9.  8.101 /  9.  8.101
  libswscale      7.  3.100 /  7.  3.100
  libswresample   4. 11.100 /  4. 11.100
  libpostproc    57.  2.100 / 57.  2.100
Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'test_mp4.mp4':
  Metadata:
    major_brand     : mp42
    minor_version   : 0
    compatible_brands: isommp42
    creation_time   : 2022-08-10T12:54:09.000000Z
  Duration: 00:00:06.67, start: 0.000000, bitrate: 567 kb/s
  Stream #0:0[0x1](eng): Video: h264 (High) (avc1 / 0x31637661), yuv420p(progressive), 384x264 [SAR 1:1 DAR 16:11], 563 kb/s, 30 fps, 30 tbr, 30k tbn (default)
    Metadata:
      creation_time   : 2022-08-10T12:54:09.000000Z
      handler_name    : Mainconcept MP4 Video Media Handler
      vendor_id       : [0][0][0][0]
      encoder         : AVC Coding
Stream mapping:
  Stream #0:0 -> #0:0 (h264 (native) -> h264 (libx264))
Press [q] to stop, [?] for help
[libx264 @ 0000018c68c8b9c0] using SAR=1/1
[libx264 @ 0000018c68c8b9c0] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2
[libx264 @ 0000018c68c8b9c0] profile High, level 2.1, 4:2:0, 8-bit
Output #0, avi, to 'pipe:':
  Metadata:
    major_brand     : mp42
    minor_version   : 0
    compatible_brands: isommp42
    ISFT            : Lavf60.6.100
  Stream #0:0(eng): Video: h264 (H264 / 0x34363248), yuv420p(progressive), 384x264 [SAR 1:1 DAR 16:11], q=2-31, 30 fps, 30 tbn (default)
    Metadata:
      creation_time   : 2022-08-10T12:54:09.000000Z
      handler_name    : Mainconcept MP4 Video Media Handler
      vendor_id       : [0][0][0][0]
      encoder         : Lavc60.17.100 libx264
    Side data:
      cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A
[out#0/avi @ 0000018c687f47c0] video:82kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 3.631060%
frame=  200 fps=0.0 q=-1.0 Lsize=      85kB time=00:00:06.56 bitrate= 106.5kbits/s speed=76.2x    
[libx264 @ 0000018c68c8b9c0] frame I:1     Avg QP:16.12  size:  3659
[libx264 @ 0000018c68c8b9c0] frame P:80    Avg QP:21.31  size:   647
[libx264 @ 0000018c68c8b9c0] frame B:119   Avg QP:26.74  size:   243
[libx264 @ 0000018c68c8b9c0] consecutive B-frames:  3.0% 53.0%  0.0% 44.0%
[libx264 @ 0000018c68c8b9c0] mb I  I16..4: 17.6% 70.6% 11.8%
[libx264 @ 0000018c68c8b9c0] mb P  I16..4:  0.8%  1.7%  0.6%  P16..4: 17.6%  4.6%  3.3%  0.0%  0.0%    skip:71.4%
[libx264 @ 0000018c68c8b9c0] mb B  I16..4:  0.1%  0.3%  0.2%  B16..8: 11.7%  1.4%  0.4%  direct: 0.6%  skip:85.4%  L0:32.0% L1:59.7% BI: 8.3%
[libx264 @ 0000018c68c8b9c0] 8x8 transform intra:59.6% inter:62.4%
[libx264 @ 0000018c68c8b9c0] coded y,uvDC,uvAC intra: 48.5% 0.0% 0.0% inter: 3.5% 0.0% 0.0%
[libx264 @ 0000018c68c8b9c0] i16 v,h,dc,p: 19% 39% 25% 17%
[libx264 @ 0000018c68c8b9c0] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 21% 25% 30%  3%  3%  4%  4%  4%  5%
[libx264 @ 0000018c68c8b9c0] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 22% 20% 16%  6%  8%  8%  8%  5%  6%
[libx264 @ 0000018c68c8b9c0] i8c dc,h,v,p: 100%  0%  0%  0%
[libx264 @ 0000018c68c8b9c0] Weighted P-Frames: Y:0.0% UV:0.0%
[libx264 @ 0000018c68c8b9c0] ref P L0: 76.2%  7.9% 11.2%  4.7%
[libx264 @ 0000018c68c8b9c0] ref B L0: 85.6% 12.9%  1.5%
[libx264 @ 0000018c68c8b9c0] ref B L1: 97.7%  2.3%
[libx264 @ 0000018c68c8b9c0] kb/s:101.19


  


  So the question is : why does this happen, and how can one avoid it ?

  


• Four Trends Shaping the Future of Analytics in Banking

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

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

   

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

   

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

  

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

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

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

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

  4 trends shaping the future of analytics in banking

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

  New privacy regulations impact banks globally

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

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

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

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

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

  

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

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

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

  The demand for personalised banking services

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

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

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

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

  The rise of AI and its role in banking

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

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

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

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

  Using analytics to minimise risk and establish a competitive edge 

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

  Use machine learning to prevent fraud

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

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

  

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

  Foresee and avoid future issues with AI-powered risk management

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

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

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

  Protect customer privacy while still getting detailed analytics

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

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

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

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

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

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

  

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

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

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

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

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

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

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

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

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

• A Quick Start Guide to the Payment Services Directive (PSD2)

  22 novembre 2024, par Daniel Crough — Banking and Financial Services, Privacy

  In 2023, there were 266.2 billion real-time payments indicating that the demand for secure transactions has never been higher. As we move towards a more open banking system, there are a host of new payment solutions that offer convenience and efficiency, but they also present new risks.

  The Payment Services Directive 2 (PSD2) is one of many regulations established to address these concerns. PSD2 is a European Union (EU) business initiative to offer smooth payment experiences while helping customers feel safe from online threats. 

  In this post, learn what PSD2 includes, how it improves security for online payments, and how Matomo supports banks and financial institutions with PSD2 compliance.

  What is PSD2 ? 

  PSD2 is an EU directive that aims to improve the security of electronic payments across the EU. It enforces strong customer authentication and allows third-party access to consumer accounts with explicit consent. 

  Its main objectives are :

  • Strengthening security and data privacy measures around digital payments.
  • Encouraging innovation by allowing third-party providers access to banking data.
  • Improving transparency with clear communication regarding fees, terms and conditions associated with payment services.
  • Establishing a framework for sharing customer data securely through APIs for PSD2 open banking.

  Rationale behind PSD2 

  PSD2’s primary purpose is to engineer a more integrated and efficient European payment market without compromising the security of online transactions. 

  The original directive aimed to standardise payment services across EU member states, but as technology evolved, an updated version was needed.

  PSD2 is mandatory for various entities within the European Economic Area (EEA), like :

  • Banks and credit institutions
  • Electronic money institutions or digital banks like Revolut
  • Card issuing and acquiring institutions
  • Fintech companies
  • Multi-national organisations operating in the EU

  PSD2 implementation timeline

  With several important milestones, PSD2 has reshaped how payment services work in Europe. Here’s a closer look at the pivotal events that paved the way for its launch.

  • 2002 : The banking industry creates the European Payments Council (EC), which drives the Single Euro Payments Area (SEPA) initiative to include non-cash payment instruments across European regions. 
  • 2007 : PSD1 goes into effect.
  • 2013 : EC proposes PSD2 to include protocols for upcoming payment services.
  • 2015 : The Council of European Union passes PSD2 and gives member states two years to incorporate it.
  • 2018 : PSD2 goes into effect. 
  • 2019 : The final deadline for all companies within the EU to comply with PSD2’s regulations and rules for strong customer authentication. 

  PSD2 : Key components 

  PSD2 introduces several key components. Let’s take a look at each one.

  Strong Customer Authentication (SCA)

  The Regulatory Technical Standards (RTS) under PSD2 outline specific requirements for SCA. 

  SCA requires multi-factor authentication for online transactions. When customers make a payment online, they need to verify their identity using at least two of the three following elements :

  • Knowledge : Something they know (like a password, a code or a secret answer)
  • Possession : Something they have (like their phone or card)
  • Inherence : Something they are (like biometrics — fingerprints or facial features)

  

  Before SCA, banks verified an individual’s identity only using a password. This dual verification allows only authorised users to complete transactions. SCA implementation reduces fraud and increases the security of electronic payments.

  SCA implementation varies for different payment methods. Debit and credit cards use the 3D Secure (3DS) protocol. E-wallets and other local payment measures often have their own SCA-compliant steps. 

  3DS is an extra step to authenticate a customer’s identity. Most European debit and credit card companies implement it. Also, in case of fraudulent chargebacks, the issuing bank becomes liable due to 3DS, not the business. 

  However, in SCA, certain transactions are exempt : 

  • Low-risk transactions : A transaction by an issuer or an acquirer whose fraud level is below a specific threshold. If the acquirer feels that a transaction is low risk, they can request to skip SCA. 
  • Low-value transactions : Transactions under €30.
  • Trusted beneficiaries : Trusted merchants customers choose to safelist.
  • Recurring payments : Recurring transactions for a fixed amount are exempt from SCA after the first transaction.

  Third-party payment service providers (TPPs) framework

  TPPs are entities authorised to access customer banking data and initiate payments. There are three types of TPPs :

  Account Information Service Providers (AISPs)

  AISPs are services that can view customers’ account details, but only with their permission. For example, a budgeting app might use AISP services to gather transaction data from a user’s bank account, helping them monitor expenses and oversee finances. 

  Payment Initiation Service Providers (PISPs)

  PISPs enable clients to initiate payments directly from their bank accounts, bypassing the need for conventional payment options such as debit or credit cards. After the customer makes a payment, PISPs immediately contact the merchant to ensure the user can access the online services or products they bought. 

  Card-Based Payment Instruments (CBPII)

  CBPIIs refer to services that issue payment cards linked to customer accounts. 

  Requirements for TPPs

  To operate effectively under PSD2, TPPs must meet several requirements :

  Consumer consent : Customers must explicitly authorise TPPs to retrieve their financial data. This way, users can control who can view their information and for what purpose.

  Security compliance : TPPs must follow SCA and secure communication guidelines to protect users from fraud and unauthorised access.

  API availability : Banks must make their Application Programming Interfaces (APIs) accessible and allow TPPs to connect securely with the bank’s systems. This availability helps in easy integration and lets TPPs access essential data. 

  Consumer protection methods

  PSD2 implements various consumer protection measures to increase trust and transparency between consumers and financial institutions. Here’s a closer look at some of these key methods :

  • Prohibition of unjustified fees : PSD2 requires banks to clearly communicate any additional charges or fees for international transfers or account maintenance. This ensures consumers are fully aware of the actual costs and charges.
  • Timely complaint resolution : PSD2 mandates that payment service providers (PSPs) have a straightforward complaint procedure. If a customer faces any problems, the provider must respond within 15 business days. This requirement encourages consumers to engage more confidently with financial services.
  • Refund in case of unauthorised payment : Customers are entitled to a full refund for payments made without their consent.
  • Surcharge ban : Additional charges on credit and debit card payments aren’t allowed. Businesses can’t impose extra fees on these payment methods, which increases customers’ purchasing power.

  Benefits of PSD2 

  Businesses — particularly those in banking, fintech, finserv, etc. — stand to benefit from PSD2 in several ways.

  Access to customer data

  With customer consent, banks can analyse spending patterns to develop tailored financial products that match customer needs, from personalised savings accounts to more relevant loan offerings.

  Innovation and cost benefits 

  PSD2 opened payment processing up to more market competition. New payment companies bring fresh approaches to banking services, making daily transactions more efficient while driving down processing fees across the sector.

  Also, banks now work alongside payment technology providers, combining their strengths to create better services. This collaboration brings faster payment options to businesses, helping them stay competitive while reducing operational costs.

  Improved customer trust and experience

  Due to PSD2 guidelines, modern systems handle transactions quickly without compromising the safety of payment data, creating a balanced approach to digital banking.

  

  Banking customers now have more control over their financial information. Clear processes allow consumers to view and adjust their financial preferences as needed.

  Strong security standards form the foundation of these new payment systems. Payment provider platforms must adhere to strict regulations and implement additional protection measures.

  Challenges in PSD2 compliance 

  What challenges can banks and financial institutions face regarding PSD2 compliance ? Let’s examine them. 

  Resource requirements

  For many businesses, the new requirements come with a high price tag. PSD2 requires banks and fintechs to build and update their systems so that other providers can access customer data safely. For example, they must develop APIs to allow TPPs to acquire customer data. 

  Many banks still use older systems that can’t meet PSD2’s added requirements. In addition to the cost of upgrades, complying with PSD2 requires banks to devote resources to training staff and monitoring compliance.

  The significant costs required to update legacy systems and IT infrastructure while keeping services running remain challenging.

  Risks and penalties

  Organisations that fail to comply with PSD2 regulations can face significant penalties.

  Additionally, the overlapping requirements of PSD2 and other regulations, such as the General Data Protection Regulation (GDPR), can create confusion. 

  Banks need clear agreements with TPPs about who’s responsible when things go wrong. This includes handling data breaches, preventing data misuse and protecting customer information. 

  Increased competition 

  Introducing new players in the financial ecosystem, such as AISPs and PISPs, creates competition. Banks must adapt their services to stay competitive while managing compliance costs.

  PSD2 aims to protect customers but the stronger authentication requirements can make banking less convenient. Banks must balance security with user experience. Focused time, effort and continuous monitoring are needed for businesses to stay compliant and competitive.

  How Matomo can help 

  Matomo gives banks and financial institutions complete control over their data through privacy-focused web analytics, keeping collected information internal rather than being used for marketing or other purposes. 

  Its advanced security setup includes access controls, audit logs, SSL encryption, single sign-on and two-factor authentication. This creates a secure environment where sensitive data remains accessible only to authorised staff.

  While prioritizing privacy, Matomo provides tools to understand user flow and customer segments, such as session recordings, heatmaps and A/B testing.

  Financial institutions particularly benefit from several key features : 

  • Tools for obtaining explicit consent before processing personal data like this Do Not Track preference
  • Insights into how financial institutions integrate TPPs (including API usage, user engagement and potential authentication drop-off points)
  • Tracking of failed login attempts or unusual access patterns
  • IP anonymization to analyse traffic patterns and detect potential fraud

  

  PSD3 : The next step 

  In recent years, we have seen the rise of innovative payment companies and increasingly clever fraud schemes. This has prompted regulators to propose updates to payment rules.

  PSD3’s scope is to adapt to the evolving digital transformation and to better handle these fraud risks. The proposed measures : 

  • Encourage PSPs to share fraud-related information.
  • Make customers aware of the different types of fraud.
  • Strengthen customer authentication standards.
  • Provide non-bank PSPs restricted access to EU payment systems. 
  • Enact payment rules in a directly applicable regulation and harmonise and enforce the directive.

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  Achieving compliance with PSD2 may be a long road for some businesses. With Matomo, organisations can enjoy peace of mind knowing their data practices align with legal requirements.

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