Nanjundan Chinnasamy’s Post

It will be good learning for those looking forward to understanding "Credit Risk Decisioning" using Pega's decision management tool🚀 Dive into advanced credit risk analysis that taps into data from multiple credit bureaus and PSD2-compliant Open Banking APIs with our solution. Ready to re-imagine your credit decision-making process for Retail and Corporate customers? Connect with DCS now! 💡 https://lnkd.in/gYDMF6WC? #WeareDCS #Pega #CreditRisk #openbanking

Last week, my spouse and I engaged in a thought-provoking discussion about the adoption and impact of #GenAI across various industries. We found it particularly interesting, and I’ve incorporated some key highlights in my article on integrating #GenerativeAI into business processes. I hope these insights resonate with you.

If you are visiting #PegaWorld, check out the DCS team (Booth#41) for #GenAI capabilities and use cases on Pega CDH and Credit Risk. DCS CreditRisk accelerator was built using the power of #Pega's RealTime Decision (to move away from vendor lock-in implementation) integration credit bureaus for your choice—of course, the demonstration of #GenAI in the #CreditRisk to address the #RiskManagers pain-points. #PegaWorldInspire #PegaWorldiNspire24 #GenAI #WeareDCS #CDH

DCS empowers banks to enhance their Credit Risk Decisioning by Pega's Real-Time Decisioning solution. Our team accelerates the implementation of best practices, ensuring efficient risk assessment and informed lending decisions for clients. Here are the benefits: ✅Simulation Capability: Enable business users to modify decision criteria, simulate the impact of changes before deployment, and deploy. This is the component that makes the Risk Managers make Informed decisions about the new changes, analyse and experience the impact of the change or implementation of a rule before the actual deployment using Production data migrated to the lower environment. ✅Use of alternate data: Using data that is traditionally not used in calculating the credit score - such as type of household, Job type, consideration of multiple incomes, and expense with their different weights. ✅Detect potential fraud: Integrating the Credit Risk Decisioning Engine to refer the External credit bureaus (such as Equifax, Experian, and TransUnion) in real-time would help us to know the holistic view of customer data and Using Open-Banking data against the loan origination system would help us to mini9minse the fraud. ✅Enhanced Data Analytics & Insights: Decision monitoring is a process of tracking and evaluating the outcomes of automated credit risk decisions. This helps to measure the performance, accuracy, and fairness of the credit decisioning models and identify areas for improvement. The drill-down functionality allows the user to explore the data in more detail by clicking on a specific report or chart and accessing the underlying data and filters. ✅Batch API Request & Response Comparison Tool: Developed a Pega tool to compare API requests and responses between old and new decision engines. Ensures zero impact on loan origination during migration. It would help Seamless decision engine migration for the clients from one decision engine to Pega. ✅Services Health Monitoring Tool: Built a portal to monitor service health and response times. Analyses payloads for failures and long execution requests. It would help banks to Proactively monitor and react to service anomalies and failures #CreditRisk #Decisioning #Pega #WeareDCS #Pegaworld

It's very interesting to read England’s women’s cricket team is embracing innovation by integrating Artificial Intelligence (AI) into their team selection strategy. AI technology, far from replacing human insight, serves as a complementary tool that enhances decision-making. By simulating numerous scenarios (250000 simulations with various combinations), AI provides valuable feedback on player matchups, which proved instrumental in England’s recent Ashes victory. WoW! This approach demonstrates the infinite potential of AI to optimise outcomes in various fields. Whether it’s Marketing, Sports, Business, or Healthcare, AI’s ability to analyse vast amounts of data can uncover patterns and insights beyond human capacity. However, the final decisions still benefit from the nice understanding that only human experience can provide. As we continue to explore the synergy between AI and human expertise, it’s clear that the most successful outcomes arise from their collaboration. AI’s role is not to replace, but to augment our capabilities, ensuring that we make the most informed choices possible analysing the vast amount of data. The future is not just AI-driven; it’s human-enhanced. Full Article link: https://lnkd.in/e5qkM4DZ

When working with Pega, capturing outbound REST API calls for auditing purposes can be a challenge. In this post, we’ll explore a generalised solution to address this issue. We’ll focus on the latest Pega versions, where direct methods for capturing raw request and response messages in connect-Rest calls are not readily available. As i am aware, there is no straightforward mechanism to capture the raw request and response messages for connect-Rest calls. Even after scouring the Pega documentation and various support articles, users often find themselves without a clear solution (apart from converting pega page to json). However, I have a workaround that involves. setting Param.storeRawMessage=true before making the connect-Rest API call. This trick, shared by Dmitry Kolesnikov (though not documented officially), allows you to capture the request and response payloads. We could see the Raw message from Clipboard, screenshot is attached below. Now, here’s where things get interesting. After successfully capturing the raw response using the method step-page, i encountered a roadblock. The pyRawMessage property, which contains the raw response data, isn’t directly accessible for reading. But tried an alternative technique involving Java code that captures the raw message for auditing. //String request = myStepPage.getStringIfPresent("pyRawMessage"); ClipboardPage cp = tools.findPage("UKPostcodeGetPage"); ClipboardProperty request = cp.getProperty("pyRawMessage"); pega_rules_utilities.sendDebugMessageToTracer("Request-->>"+request.getStringValue(),"MyRS"); Have you faced this behavior in Pega? Do you know why pyRawMessage remains elusive? Perhaps you’ve discovered other clever ways to capture the connect-rest response in its raw, unadulterated form (without converting it to a JSON string)? Looking forward to hearing from the experts. Pega support question link: https://lnkd.in/e6iAMrGv #pegainfinity #CLSA

The Role of Pega in Hyper-Personalised Marketing In the dynamic landscape of marketing, aligning technology stacks yields remarkable benefits. Many banks are aspiring to reduce response time to approximately 10-15 minutes, allowing branches or contact centers to promptly engage with interested customers from the Leads. However, achieving an in-session/in-moment level of marketing efficiency necessitated a comprehensive overhaul of our technology stack. The existing systems within the banks were most often not seamlessly interconnected, resulting in duplication, blind spots, and missed opportunities—a detrimental impact on the overall customer experience. Banks should operate to utilise real-time data to achieve in-moment marketing to increase engagement. This is not easy and requires a strategic shift that leads to substantial conversion increases, particularly in the home loans sector, and has been extended across all product lines. To achieve this, in-session marking requires collaborative efforts between the data and technology teams. Banks should establish a shared vision and systematically restructure their tech stack from the digital channels, stream the behavior data directly to the decisioning and analytics team. Tools like Pega’s Customer Decision Hub with plenty of real-time data connectors would help banks achieve hyper-personalised marketing. Pega CDH would empower the orchestration of customer interactions across all channels, ensuring that the right message reaches the right customer at precisely the right moment. This level of personalisation is instrumental in driving customer engagement and maintaining long-term retention/relationships for long-term. Let's not forget that technology like Pega Customer Decision Hub is just one piece of the puzzle, which is critical. Clean, accurate data remains essential. It's a foundation for a bank's marketing strategy.