Nicolas Pinto’s Post

Jukshio: Streamlining Onboarding and Securing the Future of BFSI The Banking, Financial Services, and Insurance (BFSI) sector is a powerhouse of innovation, constantly adapting to meet the demands of a digital world. However, this dynamism presents a unique set of challenges. Here, Jukshio dives into the key hurdles faced by BFSI institutions when it comes to customer onboarding: 1. The Inefficiency Trap: Traditional onboarding processes are riddled with unstructured processes, less-than-perfect digital infrastructure, and sometimes physical paperwork, which is a significant hassle. This creates friction for both institutions and customers. 2. Data: A Double-Edged Sword: BFSI institutions often grapple with mountains of unstructured pre-existing data. While valuable for understanding customer needs, managing and extracting insights from this data can be complex. 3. The Ever-Shifting Threatscape: Fraudsters constantly devise new techniques to exploit vulnerabilities, leading to another big challenge for the BFSI sector. Traditional security measures may not be enough to stay ahead of the curve and offer safe, secure, and seamless customer onboarding, putting sensitive customer data and financial assets at risk! 4. Striking the Privacy Balance: Robust data privacy practices are crucial for building customer trust. However, stringent regulations can sometimes create roadblocks to streamlining the onboarding journey. Finding the right balance between security and user experience is crucial. Jukshio: Your Partner in Secure Onboarding Jukshio empowers BFSI institutions to navigate these challenges with a suite of comprehensive solutions, including: Seamless Onboarding: Our secure online onboarding platform simplifies the process with intuitive interfaces and digital identity verification for a frictionless experience. Data Harmony: Jukshio's data management solutions help unlock the true potential of your existing data. Our AI allows for gaining insights to catch the existing fraudsters and potential fraud vectors. Advanced Fraud Detection: Our cutting-edge technology stays ahead of evolving threats, integrating multi-layered security protocols & real-time fraud detection to safeguard your customers. Privacy-Centric Approach: Jukshio prioritizes data privacy. We ensure compliance with regulations while fostering a secure environment that builds trust with your customers. In today's dynamic BFSI landscape, a secure and efficient onboarding process is no longer a luxury; it's a necessity. Jukshio empowers you to streamline onboarding, unlock your data's potential, and build a future of trust and security for your customers. Click to know more: www.jukshio.com Sukrit Bhattacharya Ganesh Tayi Rajasekharuni Kishore  Ribhu Ranjan Saha Taran Radia Shivani Pulimamidi Phanindra Katakam  Saurabh Chakrabarti Sridhar Tirumala Ishita Sharma

25% of card transactions are declined for reasons that have nothing to do with the available balance or credit limit, but with errors in the payment information Card transactions are an increasingly common form of payment in the world, both in physical and e-commerce. However, the information recorded in these transactions is often limited and not very descriptive, which makes data analysis and management difficult. For example, often only the amount, date and merchant code are available, but it is not known what was bought, where it was bought or who bought it. This is a problem for financial institutions, merchants and consumers, as it prevents them from exploiting the full potential of data to improve customer experience, business performance and transaction security. Some of the negative consequences of incomplete information in card transactions include the following: - Reduced customer satisfaction and loyalty by not being able to offer customers personalised products and services tailored to their needs and preferences. - Reduced business efficiency and profitability by not being able to optimise processes, resources and data-driven marketing strategies. - Increased risk of fraud and complaints, as it is not possible to verify the identity and solvency of customers, nor to detect possible anomalies or inconsistencies in payments. To address this problem, one of the most innovative and effective solutions is the enrichment of payment information with open data sources. This is a process of enhancing or supplementing existing data with additional information from public and accessible sources, such as social media, websites, open data portals or user-generated content. Enriching payment information with open data sources can have multiple benefits, for example: - Improving customer segmentation and personalisation by gaining a better understanding of customer characteristics, preferences and behaviours. - Increasing the effectiveness of marketing campaigns, by offering more appropriate and attractive products and services to each customer. - Prevent fraud and verify identity by cross-checking data against external sources and detecting possible anomalies or inconsistencies. - Enrich data analysis and visualisation by incorporating more variables and dimensions that allow more accurate and deeper conclusions to be drawn. To perform payment information enrichment with open data sources, specialised tools can be used to facilitate the search, extraction, integration and analysis of data. One such tool is Wenalyze, an open data analytics platform that allows you to enrich your customer card payment data by adding information about merchant activity. In this way, you can better understand your customers' needs and preferences, and offer them more competitive and personalised financial products and services. https://lnkd.in/dqfqShEc

An insightful scenario on how to intergrate AI tool in a customer centric environment with data protection/privacy requirements.

Anomalies in data can be costly, from faulty machinery to fraudulent activities. In 2017, improper payments in federal benefit programs alone reached a staggering $141 billion. In a more recent report by Statista e-commerce losses to online payment fraud were estimated at 41 billion U.S. dollars globally in 2022, up from the previous year. At Expeed Software, we offer tailored anomaly detection solutions, empowering businesses to cut wasteful spending and mitigate risks. Anomaly detection identifies unusual patterns, alerting systems to potential issues before they escalate. Whether it's a price glitch, broken machinery, or credit card fraud, our systems ensure timely intervention, preventing financial losses. Click here to read more about our automated anomaly detection systems. https://lnkd.in/gx9J4dRt #anomalydetection #anomaly #businessspending #riskmitigation #dataanalysis #expeedsoftware #dataanalytics #solutions

DPIA Scenario-1: Implementation of a New AI-Powered Customer Profiling System by a Bank. Background: A major bank plans to implement an AI-powered Customer Profiling System. This system will analyze customer transaction histories, online interactions, and personal financial data to offer personalized financial products, detect fraudulent activities, and improve customer service. The system will use machine learning algorithms to make predictions and decisions about customers' creditworthiness and product suitability. Why DPIA is Needed: Sensitive Financial Data: The system processes sensitive personal financial information. Automated Decision Making: There's potential for significant impact on individuals due to automated decision-making in credit scoring and product offerings. Risk of Data Breach: Given the nature of the data, there's a high risk associated with data breaches and misuse. Steps to Assess DPIA: Identify the Need for DPIA: Recognize that a DPIA is essential due to the potential impact of the new system on customer privacy and rights, especially considering the sensitivity of financial data. Describe the Processing Operations: Document how the system will collect, analyze, and use customer data. Include details on data sources, processing methodologies, and data retention practices. Assess Necessity and Proportionality: Evaluate whether the data processing is necessary for the intended purposes of improved customer service and fraud prevention, and whether the approach is proportionate to the privacy risks. Identify and Assess Privacy Risks: Consider risks related to data accuracy, unauthorized access, and the potential impacts of incorrect profiling or decision-making on customers. Consultation with Stakeholders: Consult with customers, data protection officers, financial regulators, and privacy experts to gather a range of perspectives and concerns. Mitigate Identified Risks: Develop strategies to mitigate risks, including robust encryption, strict access control, transparency in data processing, and mechanisms for customer feedback and correction of data. Record the DPIA Outcomes: Document all findings, consultations, and decisions made during the DPIA process, including how identified risks are addressed. Implement DPIA Recommendations: Ensure all the recommended safeguards are in place before the system becomes operational. Regular Review and Update: Continuously monitor the system's performance and impact on privacy, updating the DPIA as necessary to address any emerging risks or issues. In this scenario, the DPIA is critical to ensure that the bank's customer profiling system achieves its objectives without compromising customer privacy and complies with relevant data protection regulations.

𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗳𝗶𝗿𝗺𝘀 𝗿𝗲𝗰𝗼𝗴𝗻𝗶𝘇𝗲 𝗦𝗔𝗦 𝗹𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗶𝗻 𝗔𝗜 𝗮𝗻𝗱 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 SAS a leader in multiple reports for managing risk, fighting fraud and using AI to succeed. With SAS®, customers better manage risk, detect and prevent fraud, improve customer relationships, enhance data management, expand the use of AI and much more. Leading analyst firms have long recognized SAS and its technologies. This year alone, these firms have acknowledged SAS and its leadership in many important areas. Among SAS' 2023 analyst accolades to date: 𝗙𝗼𝗿𝗿𝗲𝘀𝘁𝗲𝗿 . The Forrester Wave™: Cross-Channel Marketing Hubs, Q1 2023 – SAS named a Leader (Feb. 2023). . The Forrester Wave™: AI Decisioning Platforms, Q2 2023 – SAS named a Leader in this inaugural Wave (May 2023). 𝗜𝗗𝗖 . IDC MarketScape: Asia/Pacific (Including Japan) Customer Data Platforms 2023 Vendor Assessment – SAS named a Leader (April 2023). 𝗕𝗹𝗼𝗼𝗿 . Bloor Data Quality Market Update, 2023 – SAS named a Champion (May 2023). 𝗖𝗵𝗮𝗿𝘁𝗶𝘀 . Chartis RiskTech Quadrant® for Banking Analytics Solutions, 2022 – SAS named a Leader (Jan. 2023). . Chartis RiskTech Quadrant® Model Governance Solutions – SAS named a Leader (April 2023). . Chartis RiskTech Quadrant® for Model Validation Solutions (Credit) – SAS named a Leader (April 2023). . Chartis RiskTech Quadrant® for Payment Risk Solutions (Overall), 2023 – SAS named a Leader (May 2023). . Chartis RiskTech Quadrant® for Payment Risk Solutions (Card), 2023 – SAS named a Leader (May 2023). . Chartis RiskTech Quadrant® for Payment Risk Solutions (Alternative Payments), 2023 – SAS named a Leader (May 2023). . Chartis RiskTech Quadrant® for Payment Risk Solutions (Account to Account), 2023 – SAS named a Leader (May 2023). . Chartis RiskTech Quadrant® for Credit Risk Reporting Solutions, 2023 – SAS named a Leader (May 2023). . Chartis RiskTech Quadrant® for Enterprise Fraud Solutions, 2023 – SAS named a Leader (May 2023). . Chartis STORM50: Insurance Analytics 25 2023 – SAS ranked fifth and earned two category awards (May 2023). . Chartis STORM50: Retail Finance Analytics 25 2023 – SAS ranked first and earned 11 category awards (May 2023). . Chartis STORM50: QuantTech 50 2023 – SAS ranked fifth and earned four category awards (May 2023). "Organizations today face many challenges, from turbulent economic conditions and an uneven recovery to stressed supply chains and pervasive uncertainty. Technologies like AI and advanced analytics are powerful tools for navigating challenging times," said SAS CEO 𝙹𝚒𝚖 𝙶𝚘𝚘𝚍𝚗𝚒𝚐𝚑𝚝. 𝗦𝗲𝗲 𝗺𝗼𝗿𝗲 𝗱𝗲𝘁𝗮𝗶𝗹𝘀 𝗶𝗻: https://lnkd.in/d5u2vXGH

🤔How do you optimize Payment Performance through the power of Analytics? Let me explain: So, what is payment analytics? Simply put, it's the practice of sifting through and examining the data from payment transactions. However, payment analytics goes beyond merely identifying and rectifying issues. It provides a detailed look into every step of the payment journey, unveiling not only weak spots that require resolution but also opportunities for savings and profit growth. What metrics can you track with payment analytics? 1️⃣ Payment Authorization Rate: This real-time metric denotes the percentage of successfully authorized transactions. Monitoring at both transaction attempt and order levels unveils the relationship between payment attempts and successful orders, aiding in pinpointing areas for enhancement. 2️⃣ Payment Failures: Indicative of user experience quality, this metric highlights the percentage of failed transaction attempts due to various issues, necessitating immediate corrective actions. Addressing these issues promptly can elevate the payment authorization rate and enhance the overall customer payment experience. 3️⃣ Fraud Decline Rates: Essential for monitoring potential fraudulent transactions, a high fraud decline rate may call for improved risk management. Concurrently, tracking false positives is crucial to prevent revenue loss from legitimate transactions wrongly declined. 4️⃣ Refunded Payments: This metric sheds light on the number of payments refunded, with high rates possibly pointing to product or service quality issues or fraudulent activities. Identifying the root causes enables businesses to take corrective actions, potentially boosting customer satisfaction and retention. 5️⃣ 3DS Share: Representing the share of transactions secured by 3D Secure, a discrepancy with a high number of non-3DS transactions may necessitate encouraging 3D Secure adoption among customers to mitigate fraud risk. Monitoring dropped rates due to 3DS is also vital for balancing fraud risk and authorization rates. 6️⃣ Recovery Rates: This indicates the percentage of initially failed transactions subsequently completed successfully, guiding businesses in refining their payment systems for enhanced success rates. 7️⃣ Chargeback Rates: Reflecting the percentage of transactions resulting in a chargeback, high rates may signal product quality, customer service issues, or fraud, necessitating a deeper analysis by payment method, issuer, and PSP for targeted risk management. I highly recommend reading the complete source article for more info on this topic: https://lnkd.in/eamXf5yK Find this helpful? [ 𝗿𝗲𝗽𝗼𝘀𝘁 ] Anything to add about this subject? [ 𝗶𝗻𝘃𝗶𝘁𝗲𝗱 𝘁𝗼 𝗰𝗼𝗺𝗺𝗲𝗻𝘁 ] Nice story, Marcel. Next! [ 𝗹𝗶𝗸𝗲 ]     #fintech #payments #paytech #digitalpayments #paymentservices #paymentindustry #paymentsolutions #financialtechnology #fintechindustry 

Let's talk about mule account detection. As banks become financially incentivised to root out mule accounts, the conversation around detection strategies has never been more relevant. But what elements make up a successful detection blueprint? Here's a starting list, that if executed on, likely puts you ahead of the curve: 🤝 Account Opening Scrutiny: The recruitment of once-good customers into mule accounts is a significant problem. However, accounts created with malicious intent still pose a significant risk. Reinforcing AO controls can mitigate this threat and stop mules at the front door. ⬅ Inbound Payment Monitoring: The real-time analysis of inbound payments has become a highly desirable component of mule detection. By either blocking these payments directly or using them to add context to subsequent outbound transactions, banks can adopt a proactive stance against mules. 📊 Propensity Scoring: Identifying subtle behavioural shifts is key to recognising accounts at risk of mule activity. By focusing on accounts with high-risk profiles, especially after they receive payments, banks will improve their detection rates and limit false positives. 🔀 Tying the User Journey Together: Making multiple individual decisions across the journey will move you forward but will ultimately hurt you. The real advancement comes from linking these decisions together. For instance, leveraging insights from the onboarding decision can augment the decision at the point of payment, leading to stronger detection outcomes. 🔗 Link Analysis for Network Identification: Rather than focusing solely on individual mule accounts, banks should focus on uncovering relationships through link analysis. Analysing fund transfers, shared devices, and IP addresses can uncover entire mule networks, enhancing both detection and operational efficiency. 🎓 Consumer Education: Echoing the efforts applied on scam prevention, educating consumers about the risks and consequences of becoming a mule is critical. 📈 Leveraging Data Networks: Accessing unique insights from data networks and payment processors offers a perspective that banks alone cannot achieve. Tapping into these networks to more broadly understand the risk associated with a receiving or sending account will move the bank forward analytically and improve overall detection capabilities. It will be interesting to see how this list grows as we move through 2024. What additional measures do you believe could further refine the industry approach to mule detection?

GenAI and BPM: Safeguarding Customer Assets and Enhancing the Customer Experience in Fintech The Power of GenAI in Banking Fraud Detection: A Closer Look  GenAI offers a wide array of capabilities that empower financial institutions to combat fraud with unmatched accuracy and efficiency. Let's delve into some of the key areas where GenAI truly shines, redefining the landscape of advanced fraud detection:   1. 📊 Data Analysis: GenAI leverages its deep learning capabilities to meticulously analyze vast volumes of transactional and behavioral data. This enables the system to swiftly pinpoint irregularities and anomalies that deviate from established patterns.   2. 🧩 Pattern Recognition: GenAI employs AI algorithms to extract insights from historical data, learning intricate patterns associated with legitimate transactions. Any deviations from these patterns trigger immediate alerts for further scrutiny.   3. ⏱️ Real-time Monitoring: GenAI's rapid processing capabilities enable real-time monitoring of transactions. This instantaneous analysis facilitates the swift detection of suspicious activities, thus helping prevent unauthorized transactions in their tracks.   4. 🔑 Behavioral Biometrics: GenAI's prowess extends to analyzing unique user behaviors, including typing speed, mouse movement, and navigation patterns. This ability allows it to distinguish between genuine users and potential fraudsters with remarkable accuracy.   5. 🔮 Predictive Analytics: By delving into historical data, GenAI can forecast emerging fraud trends and vulnerabilities. This predictive approach empowers financial institutions to proactively fortify their defenses against potential threats.   6. 🚫 Reduced False Positives: GenAI's continuous learning process fine-tunes its algorithms over time, minimizing false positives. This refinement ensures that legitimate customers are not inconvenienced by unnecessary fraud alerts, thus preserving the overall customer experience.   7. 🤖 Automated Responses: BPM can use GenAI to trigger automated responses, such as blocking suspicious transactions or requiring additional authentication steps, further bolstering security measures.   8. 💳 Safeguarding Customer Accounts: GenAI's proactive methodology aids in preventing unauthorized access, card testing, and other fraudulent activities, thereby safeguarding customer accounts and minimizing potential losses.

The key findings from the FCA’s review of payment account providers’ systems and controls against #moneymules activity is a must read for any PSPs and EMIs operating in the UK. The publication outlines the regulator’s key findings, including examples of good practice, areas for improvement, and how firms manage the risks of money mule accounts in a proportionate way. The FCA have observed some firms establish proportionate approaches that use innovative solutions including facial recognition systems, device profiling and geolocation. However, despite these efforts, not all firms are responding with the same focus, and some firms need to do more to tackle the problem. Focusing on onboarding and transaction monitoring, the FCA listed some main failings: ⬇ Onboarding 📌 undertaking relatively few checks at onboarding and relying on subsequently monitoring customers’ behaviour to identify suspicious activities related to money mule typologies. 📌 not capturing information such as salary or turnover details during the onboarding phase. 📌 not always conducting further investigation into the reasons why some of their customers are using virtual addresses. 📌 onboarding customers where multiple customers are using the same device with no clear reason. 📌 onboarding multiple customers using the same physical address without a reasonable and valid explanation for this. 📌 sending out cards to their customers but not checking to see if the card has been activated. ⬇ Transaction monitoring 📌 focusing on outbound transaction monitoring and do not have adequate inbound transaction monitoring systems or controls. 📌 not detecting common mule characteristics, such as high value payments into a new or (previously) dormant account and similar amounts subsequently debiting the account, as often as they should be. 📌 not leveraging device profiling, geolocation or behavioural biometrics systems in their money mules detection. 📌 heavily relying on machine learning models where required historical data to accurately understand a customer’s normal behaviour is not available. 📌 failing to continuously test the alerts that are created, ensuring these are fit for purpose and alerting on customer events as expected. 📌 failing to provide analysts with the specific criteria that triggered alerts in the machine learning tool. 📌 taking too long to implement changes to their transaction monitoring rules even where they had identified a mule characteristic or emerging risk. 📌 recording poor narratives and rationale which do not explain why the alert handler was satisfied that there were no justified suspicions. Reporting 📌 failing to report mule activity quickly and efficiently through relevant reporting systems. 📌 not respond quickly and efficiently to other firms when alerted to the receipt of fraudulent funds 📌 failing to raise a SAR at all or not raise as quickly as the regulator might expect. #amlcompliance #ffe #transactionmonitoring #aml