Fintech Integration Challenges

📢 Our latest EY AI Survey shows that financial services firms are advancing slower than expected with AI adoption due to key challenges like a lack of knowledge amongst the workforce, regulatory uncertainty, and keeping pace with GenAI evolution. Reflecting on my client conversations and the key findings from the survey below, it’ll be critical for FS firms to: ➡️ Invest strategically in infrastructure: adopt a phased approach to modernizing legacy systems ➡️ Prepare for regulatory change: build risk & control frameworks to mitigate Gen AI risks, working closely with regulatory bodies and industry experts  ➡️ Upskill the workforce: ramp-up targeted training and development programs across the workforce Get in touch with me, Ayman Awada, or Omar Ali if you want to know more and read the full findings here: https://lnkd.in/e_QxwVJG #AI #FinancialServices #ShapeTheFutureWithConfidence

Corporates continue to face significant challenges in cross-border transactions, primarily due to the limitations of the existing correspondent banking infrastructure and inconsistencies in legal, regulatory, and operational requirements across jurisdictions. The correspondent banking model, which underpins cross-border payments, inherently lacks interoperability across different national payment infrastructures. Currently, transactions must be processed sequentially through multiple intermediaries, each with unique operating hours, messaging standards, and pre-funding requirements. This creates not only delays and uncertainty in processing times but also ties up funds in nostro and vostro accounts, limiting liquidity and increasing costs. Furthermore, each intermediary adds settlement and credit risk, making the system more complex and costly to operate. Compounding these issues are the varied legal and regulatory requirements surrounding AML and CFT measures, which differ significantly across borders. Such disparities necessitate additional compliance and due diligence efforts, adding to transaction costs and delays. Additionally, the varying operational windows across domestic payment systems disrupt the flow of transactions, further hindering the speed and efficiency of global payments. Numerous public and private sector initiatives have emerged to address these pain points, achieving partial success in specific areas. The industry is yet to see a scalable, interoperable solution that can fully address these cross-border complexities. Most existing solutions are contingent upon the broader adoption of ISO 20022, with its enriched data capabilities and standardized APIs, as the foundation for a unified, transparent global payments ecosystem. However, the industry’s transition to this standard is ongoing, and the potential benefits have not yet been fully realized. Collaboration between public and private stakeholders will be crucial to achieve a harmonized cross-border payments network. Image: https://lnkd.in/gecw-jcF Nth Exception #payments #banking #iso20022 #innovation #g20

🚨 Understanding L1, L2 & L3 in Transaction Monitoring (TM) Transaction Monitoring is the backbone of Fraud Risk & AML operations — but many professionals are still unclear about how the investigation workflow actually moves across L1, L2, and L3 teams. Here’s a simple breakdown 👇 🟢 L1 – First Level Review (Alert Triage) L1 analysts handle the initial screening of system-generated alerts. They: ✔ Review customer profile & recent activity ✔ Identify false positives ✔ Check for unusual patterns (IP/location/device changes) ✔ Escalate genuine suspicious behavior Goal: Close false positives quickly and send genuine alerts upward. 🔵 L2 – Deep Investigation (Case Building) L2 analysts perform comprehensive analysis to understand the real intent behind transactions. They: ✔ Trace money flow across accounts ✔ Check beneficiary linkages ✔ Perform KYC refresh + adverse media checks ✔ Identify patterns like structuring/smurfing ✔ Build a full investigation narrative Goal: Decide whether the case is clearable or needs compliance review. 🔴 L3 – Compliance Review (Final Decision) L3 teams handle the most critical part — regulatory action. They: ✔ Review L2 findings ✔ Validate suspicious behavior ✔ File SAR/STR with regulators ✔ Liaise with legal & law enforcement ✔ Suggest rule enhancements to reduce false positives Goal: Ensure regulatory compliance and protect the financial system. 🔍 In short: 👉 L1 = Screening 👉 L2 = Investigation 👉 L3 = Compliance Decision A strong TM process depends on how well these three layers work together. 💬 If you're working in Fraud, Risk, AML, or aspire to — mastering this framework is essential. #FraudRisk #AML #TransactionMonitoring #RiskManagement #FinancialCrime #Compliance #FinTech

The reality for Payment Companies is, you do not have time. No, this isn’t a doomsday prediction—it’s a wake-up call. Payment companies (Paytechs) are at risk of missing out on transformative top-line and bottom-line gains by hesitating on GenAI adoption. What’s holding them back? According to the BCG Global Payments Report 2024, three major roadblocks constrain established companies: 1️⃣ Waiting for Certainty in the Business Case: 85% of financial services firms believe GenAI will be transformational. Yet, 74% struggle to define a clear ROI. 2️⃣ Investment Concerns: Just 26% of firms allocate significant innovation budgets to GenAI. Many fear the challenge of explaining long-term benefits to investors while balancing short-term growth. 3️⃣ Inadequate Tools and Resources: Only 18% have a defined GenAI strategy. A mere 7% have delivery teams with operational KPIs in place. But here’s the game-changer: Leading players aren’t waiting—they’re leveraging GenAI to disrupt the payments landscape: 1/ Klarna: GenAI handles 66% of customer service chats, equating to 700 employees. Resolution times dropped from 11 to 2 minutes, driving $40M in projected bottom-line improvements for 2024. 2/ Stripe: Its GenAI-powered developer portal has made it the top choice in acquiring. A multifunctional search bar summarizes documents and answers developer queries in seconds. 3/ MasterCard & Visa: GenAI enhances fraud detection, redefining the fight against financial crime. Four key opportunities stand out: 1️⃣ Customer Service & Operations: Accelerate resolutions, slash costs by up to 70%. 2️⃣ Sales & Marketing: Hyperpersonalized outreach turns “markets of one” into a reality. 3️⃣ Compliance: Real-time KYC and automated documentation redefine regulatory readiness. 4️⃣ Assisted Coding: Faster prototyping, testing, and delivery. The Time to Act Is Now While the leap of faith may seem daunting, it is essential for businesses to stay ahead. Here’s why waiting is not an option: 1/ Perfection is the enemy of progress. Companies holding out for flawless use cases risk falling further behind. Embracing continuous improvement not only accelerates institutional learning but also drives faster margin growth and business model differentiation. 2/ Build strong foundations: Strengthen data structures, acquire AI foundational AI applications, integrate core processes and prioirtise upskilling of the workforce 3/ Lead with responsibility. GenAI comes with risks—bias, errors, and intellectual property concerns. Adopting a holistic, responsible AI policy framework is non-negotiable. Yet, only 13% of companies have acted. By championing responsibility, businesses not only mitigate risks but also enhance compliance and trust. The message is clear: delaying GenAI adoption is not just a missed opportunity—it’s a competitive threat. The time to act is not yesterday, not tomorrow, but today and NOW. Are you ready to take the leap?

💥 FinCrime Mythbusters 💥 〰️ Myth#10 〰️ Cryptocurrency ❌ Myth: Cryptocurrency is completely anonymous and only used by the criminals ✔️ Reality: Cryptocurrency is pseudonymous. Every transaction is recorded on a public blockchain, creating a permanent digital trail. With the right tools, investigators can often trace flows of funds more effectively. 👉 Regulatory check ✏️ UK regulators, including the FCA and HM Treasury, treat crypto exchanges and custodian wallet providers as ‘obliged entities’ under the UK MLRs. This means they must conduct KYC checks, monitor transactions, and report suspicious activity. ✏️ Blockchain analytics firms have helped trace ransomware payments, sanctions breaches, and even funds linked to terrorism. For example, the takedown of darknet marketplaces has often relied on following Bitcoin trails. ✏️ While criminals do exploit privacy coins, mixers, and cross-chain swaps to obscure funds, regulators are catching up. FATF’s ‘Travel Rule’ and the UK’s implementation of it are aimed at reducing anonymity in crypto transfers. 🛠️ How Crypto challenges legacy transaction monitoring systems? 🖍️ Data mismatch: Legacy TMS consume structured banking data. Crypto transactions are pseudonymous wallet addresses and hashes. Without blockchain analytics integration, red flags go unseen. 🖍️ Identity gaps: Banks monitor verified customers, however in crypto; the counterparty could just be a wallet address. UK MLRs force exchanges to KYC customers, but self-hosted wallets continue to remain a blind spot. 🖍️ New risk typologies: Traditional rule-based systems continue looking for structuring or cross-border fiat layering. Crypto introduces mixers, cross-chain swaps, and DeFi obfuscation. 🖍️ Speed & scale: Fiat monitoring often runs in daily batches. Crypto moves in seconds, 24/7. By the time an alert triggers, funds may already be through five wallets and a mixer 🌪️ 🏹 A little story I have worked on a series of crypto SARs where money mules are tricked into moving money, believing they are helping with a job or a relationship. I have seen victims fall for fake 'investment platforms' and lose everything before they even realized what happened. Don't consider SAR as a paperwork, it is a chance to catch the pattern, break the chain ⛓️, and protect the next person from being exploited. Park the crypto transactions leaving via MSB's and see how it unfolds 🔓 ⚔️ Crypto does not replace legacy transaction monitoring, it infact exposes its limitations. Without blockchain analytics and new typology libraries, traditional systems risk becoming as outdated as a Valyrian steel sword left to rust. #FinCrimeMythbusters #AML #TransactionMonitoring #RiskCoverage #FinancialCrimePrevention #TMStrategy #RegTech #cryptocurrency

The Wolfsberg Group has issued a new Statement on Monitoring for Suspicious Activity (MSA). The statement recognises that criminal networks “continue to evolve at a rapid pace.” As a result, both national security priorities and financial institutions’ innovation governance frameworks must evolve accordingly. It makes a significant distinction between: ↳ Transaction Monitoring (TM) - rule-based, transaction-focused, and heavily reliant on fixed thresholds. ↳ Monitoring for Suspicious Activity (MSA) - data-driven, outcome-focused, and informed by a broader set of inputs: • customer behaviour • typologies • contextual risk indicators • and transactional activity combined Traditional TM detects pre-defined transactional patterns - such as cash structuring or high-value wire transfers. But many risks cannot be identified through transactions alone. The Wolfsberg Group highlights that real suspicion often emerges when transactions are viewed in context - alongside customer profiles, behavioural patterns, and known typologies. 𝗪𝗵𝗮𝘁 𝗿𝗶𝘀𝗸𝘀 𝗱𝗼𝗲𝘀 𝘁𝗵𝗲 𝗪𝗼𝗹𝗳𝘀𝗯𝗲𝗿𝗴 𝗚𝗿𝗼𝘂𝗽 𝗵𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁? → Financial institutions relying solely on rules-based TM may be missing high-impact risks. → Innovation is slowed by governance frameworks designed for prudential risk, not financial crime. → Low-quality alerts and SARs continue to burden investigators and add limited value to law enforcement. 𝗪𝗵𝗮𝘁 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝗱𝗼𝗲𝘀 𝘁𝗵𝗲 𝘀𝘁𝗮𝘁𝗲𝗺𝗲𝗻𝘁 𝗿𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱? The Wolfsberg Group outlines a transition framework based on three pillars: 1️⃣ 𝗧𝗿𝗮𝗻𝘀𝗶𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝘃𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻: FIs should update their approach based on redefined outcomes - not legacy system performance. The goal is to improve precision and relevance, not to replicate outdated alerts. 2️⃣ 𝗕𝗮𝗹𝗮𝗻𝗰𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹 𝗿𝗶𝘀𝗸 𝘄𝗶𝘁𝗵 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗰𝗿𝗶𝗺𝗲 𝗿𝗶𝘀𝗸: AML models should not be governed like credit or market risk models. Financial crime risks require adaptability, and excessive oversight can hinder timely implementation. 3️⃣ 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Institutions must be able to clearly explain how models work, what risks they cover, and how analysts can use outputs to support meaningful investigations. 𝗪𝗵𝗮𝘁 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁𝘀 𝗰𝗮𝗻 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗶𝗻𝘀𝘁𝗶𝘁𝘂𝘁𝗶𝗼𝗻𝘀 𝗲𝘅𝗽𝗲𝗰𝘁 𝗯𝘆 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗶𝗻𝗴 𝘁𝗵𝗲𝘀𝗲 𝗿𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀? ✔ Better alignment with law enforcement priorities ✔ Improved quality of suspicious activity reporting ✔ More effective use of data and analytics across the institution ✔ Stronger ability to detect emerging risks - not just known patterns The message is clear: Effective financial crime monitoring is no longer about catching what’s obvious. It’s about uncovering what’s relevant. Is your institution prepared for this change?

For most of the last century, generators stabilised the grid as a by-product of producing energy. Today, we are building assets that stabilise the grid without producing energy at all. That shift identifies the binding constraint. Electricity system transition is no longer constrained by renewable resource availability. It is constrained by deliverability and operability. In inverter-dominated systems under rapid load growth, the binding constraints are: - transmission and major substation capacity - system strength, fault levels, frequency and voltage control - connection and commissioning throughput - secure operation under worst-day conditions - execution pace across networks and system services Generation capacity remains necessary. On its own, it no longer delivers firm supply or supports large new loads. Historically, synchronous generators supplied energy and stability together. Inertia, fault current, voltage support, and controllability were implicit. As synchronous plant retires, these services must be provided explicitly. Stability shifts from physics-led to control-led. System behaviour becomes more sensitive to modelling accuracy, protection coordination, control settings, and real-time visibility. Curtailment is not excess energy. It is a deliverability or security constraint. When transmission and substations lag generation, congestion and curtailment rise. Independent analysis shows that delay increases prices and emissions by extending reliance on higher-cost thermal generation. Distribution networks are no longer passive. They now host distributed generation, storage, EV charging, and large loads at the edge of transmission. Voltage control, protection coordination, hosting capacity, and connection throughput now constrain both decarbonisation and industrial growth. Firming is a hard requirement. Batteries provide fast frequency response and contingency arrest. They do not provide multi-day energy and do not replace networks or system strength in weak grids. Demand response reduces peaks. It cannot be relied upon for system-wide security under stress. Execution speed is critical. Slow delivery increases congestion duration, curtailment exposure, reserve requirements, and reliance on ageing plant. These effects flow directly into costs, emissions, and reliability. This is why electricity bills can rise even when average wholesale prices fall. Costs are driven by peak demand, contingencies, and security, not average energy. Large digital and industrial loads are transmission-scale, continuous, and failure-intolerant. They increase contingency size and correlation risk. At that scale, loads do not connect to the grid, they shape it. Supporting growth requires time-to-power, transmission and substation capacity in load corridors, explicit system strength and fault levels, operable firming under worst-day conditions, scalable connection and commissioning, and early procurement of long lead time HV equipment. #energy

𝐓𝐡𝐞 𝐁𝐥𝐮𝐞𝐩𝐫𝐢𝐧𝐭 𝐟𝐨𝐫 𝐀𝐈 𝐌𝐞𝐭𝐫𝐢𝐜𝐬 𝐓𝐡𝐚𝐭 𝐀𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐃𝐫𝐢𝐯𝐞 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐕𝐚𝐥𝐮𝐞 AI metrics should drive Business Outcomes, not just Measure Performance.  Here is the Framework that aligns AI Metrics with Real-World value: 1. THE BLUEPRINT Three pillars: Decision Impact + Operational Reliability + Human Trust. Example: A claims agent that approves low-risk claims, escalates edge cases, and keeps humans in control. 2. NORTH STAR METRIC Pick one metric that captures value in production. • Net value per decision ↳ Fraud agent prevents $25 loss per case, costs $4 to run/review. Net value = $21. • Regret rate (% of decisions reversed) ↳ Out of 10,000 recommendations, 800 are changed by humans. Regret rate = 8%. • Revenue impact ↳ AI routing lifts conversion from 2.0% to 2.3% on 1M visits (3,000 extra conversions). • Cost per correct action ↳ Monthly run cost $200K / 400K correct actions = $0.50 per action. 3. DATA Leverage post-launch signals to understand behavior. • Decisions & outcomes ↳ Tracking "Approve claim" vs. whether it later became a chargeback. • Overrides & appeals ↳ Agent rejects refund → customer appeals → human approves. (Log this loop!) • Latency & failures ↳ P95 latency spikes during peak hours causing tool call timeouts. 4. CONSTRAINTS Constraints define what is sustainable at scale. Internal: • Review capacity: Your team can review 500 escalations/day. If the model sends 1,200, you bottleneck. • Infra cost: A "better" model doubles quality but triples cost per case. ROI drops. • Latency: Agent assist must respond under 800 ms to be usable. External: • Market behavior: Fraud patterns shift after you deploy. • User adaptation: Reps stop trusting suggestions after two bad calls, even if accuracy is high. 5. IDEATION + PRIORITIZATION Generate metric-driven improvements. • Impact vs risk: Automate low-risk approvals first. Keep high-risk human-led. • Regret frequency: 60% of overrides come from document parsing? Fix that first. • Drift severity: Regret rate rises from 6% to 11%? Roll back or retrain. • Cost vs value: Add a retrieval step that costs $0.02 but cuts regret by 20%. 6. EXPERIMENTATION Run controlled changes on: • Thresholds: Raise confidence threshold so fewer cases auto-approve. • Escalation rules: Escalate when the model disagrees with policy rules. • Model versions: A/B test smaller model vs larger model on "cost per correct action." MY RECOMMENDATION AI metrics aren't about model performance, they're about business value. Measure what drives decisions, not what's easy to measure. Track regret, not just accuracy.  Track value, not just speed.  Track adoption, not just deployment. Which metric are you tracking that does not drive business value? PS: If you found this valuable, join my weekly newsletter where I document the real-world journey of AI transformation. ✉️ Free subscription: https://lnkd.in/exc4upeq #GenAI #EnterpriseAI #AgenticAI