PSD2: How machine learning reduces friction and satisfies SCA – The Paypers
Andy Renshaw, Feedzai: It crosses borders but doesnt have a passport. Its meant to protect people but can make them angry. Its competitive by nature but doesnt want you to fail. What is it?
If the PSD2 regulations and Strong Customer Authentication (SCA) feel like a riddle to you, youre not alone. SCA places strict two-factor authentication requirements upon financial institutions (FIs) at a time when FIs are facing stiff competition for customers. On top of that, the variety of payment types, along with the sheer number of transactions, continue to increase.
According to UK Finance, the number of debit card transactions surpassed cash transactions since 2017, while mobile banking surged over the past year, particularly for contactless payments. The number of contactless payment transactions per customer is growing; this increase in transactions also raises the potential for customer friction.
The number of transactions isnt the only thing thats shown an exponential increase; the speed at which FIs must process them is too. Customers expect to send, receive, and access money with the swipe of a screen. Driven by customer expectations, instant payments are gaining traction across the globe with no sign of slowing down.
Considering the sheer number of transactions combined with the need to authenticate payments in real-time, the demands placed on FIs can create a real dilemma. In this competitive environment, how can organisations reduce fraud and satisfy regulations without increasing customer friction?
For countries that fall under PSD2s regulation, the answer lies in the one known way to avoid customer friction while meeting the regulatory requirement: keep fraud rates at or below SCA exemption thresholds.
How machine learning keeps fraud rates below the exemption threshold to bypass SCA requirements
Demonstrating significantly low fraud rates allows financial institutions to bypass the SCA requirement. The logic behind this is simple: if the FIs systems can prevent fraud at such high rates, they've demonstrated their systems are secure without authentication.
SCA exemption thresholds are:
Exemption Threshold Value
Remote electronic card-based payment
Remote electronic credit transfers
EUR 500
below 0.01% fraud rate
below 0.01% fraud rate
EUR 250
below 0.06% fraud rate
below 0.01% fraud rate
EUR 100
below 0.13% fraud rate
below 0.015% fraud rate
Looking at these numbers, you might think that achieving SCA exemption thresholds is impossible. After all, bank transfer scams rose 40% in the first six months of 2019. But state-of-the-art technology rises to the challenge of increased fraud. Artificial intelligence, and more specifically machine learning, makes achieving SCA exemption thresholds possible.
How machine learning achieves SCA exemption threshold values
Every transaction has hundreds of data points, called entities. Entities include time, date, location, device, card, cardless, sender, receiver, merchant, customer age the possibilities are almost endless. When data is cleaned and connected, meaning it doesnt live in siloed systems, the power of machine learning to provide actionable insights on that data is historically unprecedented.
Robust machine learning technology uses both rules and models and learns from both historical and real-time profiles of virtually every data point or entity in a transaction. The more data we feed the machine, the better it gets at learning fraud patterns. Over time, the machine learns to accurately score transactions in less than a second without the need for customer authentication.
Machine learning creates streamlined and flexible workflows
Of course, sometimes, authentication is inevitable. For example, if a customer who generally initiates a transaction in Brighton, suddenly initiates a transaction from Mumbai without a travel note on the account, authentication should be required. But if machine learning platforms have flexible data science environments that embed authentication steps seamlessly into the transaction workflow, the experience can be as customer-centric as possible.
Streamlined workflows must extend to the fraud analysts job
Flexible workflows arent just important to instant payments theyre important to all payments. And they cant just be a back-end experience in the data science environment. Fraud analysts need flexibility in their workflows too. They're under pressure to make decisions quickly and accurately, which means they need a full view of the customer not just the transaction.
Information provided at a transactional level doesnt allow analysts to connect all the dots. In this scenario, analysts are left opening up several case managers in an attempt to piece together a complete and accurate fraud picture. Its time-consuming and ultimately costly, not to mention the wear and tear on employee satisfaction. But some machine learning risk platforms can show both authentication and fraud decisions at the customer level, ensuring analysts have a 360-degree view of the customer.
Machine learning prevents instant payments from becoming instant losses
Instant payments can provide immediate customer satisfaction, but also instant fraud losses. Scoring transactions in real-time means institutions can increase the security around the payments going through their system before its too late.
Real-time transaction scoring requires a colossal amount of processing power because it cant use batch processing, an efficient method when dealing with high volumes of data. Thats because the lag time between when a customer transacts and when a batch is processed makes this method incongruent with instant payments. Therefore, scoring transactions in real-time requires supercomputers with super processing powers. The costs associated with this make hosting systems on the cloud more practical than hosting at the FIs premises, often referred to as on prem. Of course, FIs need to consider other factors, including cybersecurity concerns before determining where they should host their machine learning platform.
Providing exceptional customer experiences by keeping fraud at or below PSD2s SCA threshold can seem like a magic trick, but its not. Its the combined intelligence of humans and machines to provide the most effective method we have today to curb and prevent fraud losses. Its how we solve the friction-security puzzle and deliver customer satisfaction while satisfying SCA.
About Andy Renshaw
Andy Renshaw, Vice President of Banking Solutions at Feedzai, has over 20 years of experience in banking and the financial services industry, leading large programs and teams in fraud management and AML. Prior to joining Feedzai, Andy held roles in global financial institutions such as Lloyds Banking Group, Citibank, and Capital One, where he helped fight against the ever-evolving financial crime landscape as a technical expert, fraud prevention expert, and a lead product owner for fraud transformation.
About Feedzai
Feedzai is the market leader in fighting fraud with AI. Were coding the future of commerce with todays most advanced risk management platform powered by big data and machine learning. Founded and developed by data scientists and aerospace engineers, Feedzai has one mission: to make banking and commerce safe. The worlds largest banks, processors, and retailers use Feedzais fraud prevention and anti-money laundering products to manage risk while improving customer experience.
More here:
PSD2: How machine learning reduces friction and satisfies SCA - The Paypers
- 3D Shape Tokenization - Apple Machine Learning Research - January 9th, 2025 [January 9th, 2025]
- Machine Learning Used To Create Scalable Solution for Single-Cell Analysis - Technology Networks - January 9th, 2025 [January 9th, 2025]
- Robotics: machine learning paves the way for intuitive robots - Hello Future - January 9th, 2025 [January 9th, 2025]
- Machine learning-based estimation of crude oil-nitrogen interfacial tension - Nature.com - January 9th, 2025 [January 9th, 2025]
- Machine learning Nomogram for Predicting endometrial lesions after tamoxifen therapy in breast Cancer patients - Nature.com - January 9th, 2025 [January 9th, 2025]
- Staying ahead of the automation, AI and machine learning curve - Creamer Media's Engineering News - January 9th, 2025 [January 9th, 2025]
- Machine Learning and Quantum Computing Predict Which Antibiotic To Prescribe for UTIs - Consult QD - January 9th, 2025 [January 9th, 2025]
- Machine Learning, Innovation, And The Future Of AI: A Conversation With Manoj Bhoyar - International Business Times UK - January 9th, 2025 [January 9th, 2025]
- AMD's FSR 4 will use machine learning but requires an RDNA 4 GPU, promises 'a dramatic improvement in terms of performance and quality' - PC Gamer - January 9th, 2025 [January 9th, 2025]
- Explainable artificial intelligence with UNet based segmentation and Bayesian machine learning for classification of brain tumors using MRI images -... - January 9th, 2025 [January 9th, 2025]
- Understanding the Fundamentals of AI and Machine Learning - Nairobi Wire - January 9th, 2025 [January 9th, 2025]
- Machine learning can help blood tests have a separate normal for each patient - The Hindu - January 1st, 2025 [January 1st, 2025]
- Artificial Intelligence and Machine Learning Programs Introduced this Spring - The Flash Today - January 1st, 2025 [January 1st, 2025]
- Virtual reality-assisted prediction of adult ADHD based on eye tracking, EEG, actigraphy and behavioral indices: a machine learning analysis of... - January 1st, 2025 [January 1st, 2025]
- Open source machine learning systems are highly vulnerable to security threats - TechRadar - December 22nd, 2024 [December 22nd, 2024]
- After the PS5 Pro's less dramatic changes, PlayStation architect Mark Cerny says the next-gen will focus more on CPUs, memory, and machine-learning -... - December 22nd, 2024 [December 22nd, 2024]
- Accelerating LLM Inference on NVIDIA GPUs with ReDrafter - Apple Machine Learning Research - December 22nd, 2024 [December 22nd, 2024]
- Machine learning for the prediction of mortality in patients with sepsis-associated acute kidney injury: a systematic review and meta-analysis - BMC... - December 22nd, 2024 [December 22nd, 2024]
- Machine learning uncovers three osteosarcoma subtypes for targeted treatment - Medical Xpress - December 22nd, 2024 [December 22nd, 2024]
- From Miniatures to Machine Learning: Crafting the VFX of Alien: Romulus - Animation World Network - December 22nd, 2024 [December 22nd, 2024]
- Identification of hub genes, diagnostic model, and immune infiltration in preeclampsia by integrated bioinformatics analysis and machine learning -... - December 22nd, 2024 [December 22nd, 2024]
- This AI Paper from Microsoft and Novartis Introduces Chimera: A Machine Learning Framework for Accurate and Scalable Retrosynthesis Prediction -... - December 18th, 2024 [December 18th, 2024]
- Benefits and Challenges of Integrating AI and Machine Learning into EHR Systems - Healthcare IT Today - December 18th, 2024 [December 18th, 2024]
- The History Of AI: How Machine Learning's Evolution Is Reshaping Everything Around Us - SlashGear - December 18th, 2024 [December 18th, 2024]
- AI and Machine Learning to Enhance Pension Plan Governance and the Investor Experience: New CFA Institute Research - Fintech Finance - December 18th, 2024 [December 18th, 2024]
- Address Common Machine Learning Challenges With Managed MLflow - The New Stack - December 18th, 2024 [December 18th, 2024]
- Machine Learning Used To Classify Fossils Of Extinct Pollen - Offworld Astrobiology Applications? - Astrobiology News - December 18th, 2024 [December 18th, 2024]
- Machine learning model predicts CDK4/6 inhibitor effectiveness in metastatic breast cancer - News-Medical.Net - December 18th, 2024 [December 18th, 2024]
- New Lockheed Martin Subsidiary to Offer Machine Learning Tools to Defense Customers - ExecutiveBiz - December 18th, 2024 [December 18th, 2024]
- How Powerful Will AI and Machine Learning Become? - International Policy Digest - December 18th, 2024 [December 18th, 2024]
- ChatGPT-Assisted Machine Learning for Chronic Disease Classification and Prediction: A Developmental and Validation Study - Cureus - December 18th, 2024 [December 18th, 2024]
- Blood Tests Are Far From Perfect But Machine Learning Could Change That - Inverse - December 18th, 2024 [December 18th, 2024]
- Amazons AGI boss: You dont need a PhD in machine learning to build with AI anymore - Fortune - December 18th, 2024 [December 18th, 2024]
- From Novice to Pro: A Roadmap for Your Machine Learning Career - KDnuggets - December 10th, 2024 [December 10th, 2024]
- Dimension nabs $500M second fund for 'still contrary' intersection of bio and machine learning - Endpoints News - December 10th, 2024 [December 10th, 2024]
- Using Machine Learning to Make A Really Big Detailed Simulation - Astrobites - December 10th, 2024 [December 10th, 2024]
- Driving Business Growth with GreenTomatos Data and Machine Learning Strategy on Generative AI - AWS Blog - December 10th, 2024 [December 10th, 2024]
- Unlocking the power of data analytics and machine learning to drive business performance - WTW - December 10th, 2024 [December 10th, 2024]
- AI and the Ethics of Machine Learning | by Abwahabanjum | Dec, 2024 - Medium - December 10th, 2024 [December 10th, 2024]
- Differentiating Cystic Lesions in the Sellar Region of the Brain Using Artificial Intelligence and Machine Learning for Early Diagnosis: A Prospective... - December 10th, 2024 [December 10th, 2024]
- New Amazon SageMaker AI Innovations Reimagine How Customers Build and Scale Generative AI and Machine Learning Models - Amazon Press Release - December 10th, 2024 [December 10th, 2024]
- What is Machine Learning? 18 Crucial Concepts in AI, ML, and LLMs - Netguru - December 5th, 2024 [December 5th, 2024]
- Machine learning-based prediction of antibiotic resistance in Mycobacterium tuberculosis clinical isolates from Uganda - BMC Infectious Diseases - December 5th, 2024 [December 5th, 2024]
- Interdisciplinary Team Needed to Apply Machine Learning in Epilepsy Surgery: Lara Jehi, MD, MHCDS - Neurology Live - December 5th, 2024 [December 5th, 2024]
- A multimodal machine learning model for the stratification of breast cancer risk - Nature.com - December 5th, 2024 [December 5th, 2024]
- Machine learning based intrusion detection framework for detecting security attacks in internet of things - Nature.com - December 5th, 2024 [December 5th, 2024]
- Machine learning evaluation of a hypertension screening program in a university workforce over five years - Nature.com - December 5th, 2024 [December 5th, 2024]
- Vaultree Introduces VENum Stack: Combining the Power of Machine Learning and Encrypted Data Processing for Secure Innovation - PR Newswire - December 5th, 2024 [December 5th, 2024]
- Direct simulation and machine learning structure identification unravel soft martensitic transformation and twinning dynamics - pnas.org - December 5th, 2024 [December 5th, 2024]
- AI and Machine Learning - Maryland to use AI technology to manage traffic flow - SmartCitiesWorld - December 5th, 2024 [December 5th, 2024]
- Researchers make machine learning breakthrough in lithium-ion tech here's how it could make aging batteries safer - Yahoo! Voices - December 5th, 2024 [December 5th, 2024]
- Integrating IoT and machine learning: Benefits and use cases - TechTarget - December 5th, 2024 [December 5th, 2024]
- Landsat asks industry for artificial intelligence (AI) and machine learning for satellite operations - Military & Aerospace Electronics - December 5th, 2024 [December 5th, 2024]
- Machine learning optimized efficient graphene-based ultra-broadband solar absorber for solar thermal applications - Nature.com - December 5th, 2024 [December 5th, 2024]
- Polymathic AI Releases The Well: 15TB of Machine Learning Datasets Containing Numerical Simulations of a Wide Variety of Spatiotemporal Physical... - December 5th, 2024 [December 5th, 2024]
- Prediction of preterm birth using machine learning: a comprehensive analysis based on large-scale preschool children survey data in Shenzhen of China... - December 5th, 2024 [December 5th, 2024]
- Application of machine learning algorithms to identify serological predictors of COVID-19 severity and outcomes - Nature.com - November 30th, 2024 [November 30th, 2024]
- Predicting the time to get back to work using statistical models and machine learning approaches - BMC Medical Research Methodology - November 30th, 2024 [November 30th, 2024]
- AI and Machine Learning - US releases recommendations for use of AI in critical infrastructure - SmartCitiesWorld - November 30th, 2024 [November 30th, 2024]
- Machine learning-based diagnostic model for stroke in non-neurological intensive care unit patients with acute neurological manifestations -... - November 28th, 2024 [November 28th, 2024]
- Analysis of four long non-coding RNAs for hepatocellular carcinoma screening and prognosis by the aid of machine learning techniques - Nature.com - November 28th, 2024 [November 28th, 2024]
- Evaluation and prediction of the physical properties and quality of Jatob-do-Cerrado seeds processed and stored in different conditions using machine... - November 28th, 2024 [November 28th, 2024]
- Researchers use fitness tracker data and machine learning to detect bipolar disorder mood swings - Medical Xpress - November 28th, 2024 [November 28th, 2024]
- Advances in AI and Machine Learning for Nuclear Applications - Frontiers - November 28th, 2024 [November 28th, 2024]
- Researchers make machine learning breakthrough in lithium-ion tech here's how it could make aging batteries safer - The Cool Down - November 28th, 2024 [November 28th, 2024]
- Svitla Systems Publishes Results of the Study on Machine Learning's Role in Credit Scoring - Newsfile - November 28th, 2024 [November 28th, 2024]
- Predicting poor performance on cognitive tests among older adults using wearable device data and machine learning: a feasibility study - Nature.com - November 28th, 2024 [November 28th, 2024]
- Quantum Machine Learning: Bridging the Future of AI and Quantum Computing - TechBullion - November 28th, 2024 [November 28th, 2024]
- AI and machine learning trends in healthcare - Healthcare Leader - November 28th, 2024 [November 28th, 2024]
- Identification of biomarkers for the diagnosis in colorectal polyps and metabolic dysfunction-associated steatohepatitis (MASH) by bioinformatics... - November 28th, 2024 [November 28th, 2024]
- Revolutionizing Business Systems with Machine Learning: Practical Innovations for the Modern Era - TechBullion - November 28th, 2024 [November 28th, 2024]
- Can AI improve plant-based meats? Using mechanical testing and machine learning to mimic the sensory experience - Phys.org - November 16th, 2024 [November 16th, 2024]
- Machine Learning Reveals Impact of Microbial Load on Gut Health and Disease - Genetic Engineering & Biotechnology News - November 16th, 2024 [November 16th, 2024]
- Machine learning for predicting in-hospital mortality in elderly patients with heart failure combined with hypertension: a multicenter retrospective... - November 16th, 2024 [November 16th, 2024]
- Apple Researchers Propose Cut Cross-Entropy (CCE): A Machine Learning Method that Computes the Cross-Entropy Loss without Materializing the Logits for... - November 16th, 2024 [November 16th, 2024]
- Exploring electron-beam induced modifications of materials with machine-learning assisted high temporal resolution electron microscopy - Nature.com - November 16th, 2024 [November 16th, 2024]
- Facilitated the discovery of new / Co-based superalloys by combining first-principles and machine learning - Nature.com - November 16th, 2024 [November 16th, 2024]
- Thwarting Phishing Attacks with Predictive Analytics and Machine Learning in 2024 - Petri.com - November 16th, 2024 [November 16th, 2024]
- Optoelectronic performance prediction of HgCdTe homojunction photodetector in long wave infrared spectral region using traditional simulations and... - November 16th, 2024 [November 16th, 2024]
- A new approach for sex prediction by evaluating mandibular arch and canine dimensions with machine-learning classifiers and intraoral scanners (a... - November 16th, 2024 [November 16th, 2024]