Cognitive Risk Intelligence and Control System

[Audio] Hello everyone! This is Team Risk -e- Ai, and today, for this presentation, I — your AI host — have been given the exciting task of taking you on a journey into the world of an AI-powered system. This isn't just any system — it's one that promises to solve your organization's most pressing challenges. But what are those challenges exactly? And how does this system rise to meet them? Well, stay with me — we're about to find out..

[Audio] These are the contents of today's presentation, which we will go through one by one..

[Audio] Today, risk and compliance functions in financial institutions are facing a silent crisis — not because of a lack of data, but because of how it's managed and interpreted. Imagine this: A risk analyst is flooded with alerts, 90% of which are false positives. Investigators spend hours, sometimes days, sifting through fragmented tools and isolated systems just to validate a single anomaly. Meanwhile, real threats — like ghost borrowers or evolving fraud patterns — slip through unnoticed because the system can't adapt fast enough. These outdated rule-based systems were once the gold standard. But now, they're like trying to fight cybercrime with a rotary phone. Let's talk about the five key challenges that are dragging compliance teams down: False Positives Overload: Rigid rule engines generate more noise than signals, drowning teams in irrelevant alerts. Manual Bottlenecks: Investigations are slow, siloed, and heavily dependent on human effort — causing delays and oversight. Scattered Datasets: Information is fragmented across onboarding portals, transaction logs, emails, and chat records — making holistic analysis nearly impossible. Siloed Operations: Risk, compliance, and operations don't talk to each other. They're fragmented teams with fragmented tools. Lack of Proactive Intelligence: Traditional systems react after something happens. They don't evolve, they don't predict, and they definitely don't prevent. And this brings us to the problem statement: Financial institutions are navigating a growing ocean of vast, diverse, and unstructured data — using static, outdated systems. The result? High false positives, slow insights, and costly inefficiencies. But what if we could flip the script? What if compliance didn't just react to problems — but predicted and prevented them? That's exactly where our solution CRICS comes in. And in the next few minutes, I'll show you how this AI-driven architecture transforms risk and compliance from a cost centre to a competitive advantage..

[Audio] To address the challenges, we introduce 'CRICS' which stands for Cognitive Risk Intelligence & Control System. This AI-powered solution aims to digitally transform how financial institutions handle risk, compliance, and regulatory oversight. It brings together four intelligent components: Generative AI to analyze policies, identify control gaps, and recommend actions, Agentic AI for real-time alerting, triage, and autonomous investigation, RAG (Retrieval-Augmented Generation) for delivering context-aware, document-driven insights, And Multimodal Context Analysis to decode communication patterns and detect anomalies across diverse data formats. Target industries for CRICS could be banking, insurance, fintech, investment firms, and regulatory bodies — helping them move from reactive compliance to proactive risk management. With its scalable, explainable architecture and human-in-the-loop design, CRICS enhances operational efficiency, reduces risk exposure, and empowers smarter, faster decision-making. In short, CRICS doesn't just respond to risk — it anticipates it, making your compliance framework intelligent, adaptive, and future-ready..

[Audio] This is the high-level business architecture of CRICS — a layered system powered by AI agents. It begins with Data Ingestion, where CRICS collects structured and unstructured data from multiple sources and validates it. Then comes Data Processing, where the system analyzes the data to detect anomalies, violations, and policy breaches. The Learning & Adaptation module refines the models continuously using feedback, ensuring evolving compliance. At the heart of the system is the Core AI Agent, which uses large language models to reason, plan, and act on data. Decisions flow into the Orchestration Layer, where actions are executed via the Distribution and Compute Engines. Finally, insights are delivered through intuitive dashboards, and outcomes are compiled into audit-ready reporting for compliance teams and regulators. CRICS's modular, intelligent architecture ensures traceability, adaptability, and real-time decision-making at scale..

[Audio] The CRICS architecture is divided into several key layers that work in concert: · Front-End Layer: This layer serves as the user interaction point, primarily through a Web Portal and user interfaces for Compliance Officers, Risk Analysts, and Investigators. · Application Layer: This is the core of the CRICS system, powered by Core AI Agents that are responsible for reasoning, planning, and acting. It includes a Reasoning Engine for contextual interpretation, leveraging Large Language Models (LLMs) such as GPT and BERT. A Knowledge Repository and a Vector Database are also present, which, along with text embedding models, support contextual policy retrieval and decision support for the LLMs. · Core Orchestration Engine: This component is central to managing the system's operations. It handles workflow control, task prioritization, and error handling, ensuring smooth execution of processes. · Planning Engine: This engine is responsible for decision orchestration and utilizes Dynamic Planning Models, including POMDP (Partially Observable Markov Decision Process) and MDP (Markov Decision Process), to strategize actions. · Action Execution Layer: This layer facilitates the fulfillment of tasks through a Task Fulfilment Engine, which orchestrates API calls via API Orchestration Gateways. · Back-End Layer: This layer manages the routing of requests and includes a Distribution Engine with an API Gateway, Service Routing, and Load Balancer for efficient request handling. It also contains a Compute Engine responsible for LLM processing, model execution, and resource scaling. · CRICS AI Agents: These specialized agents perform various functions within the system: Data Ingestion Agent: Validates data and performs integrity checks before feeding it into the system. Learning and Adaption Agent: Monitors ethical and compliance aspects and performs model mitigation. Reporting Agent: Generates feeds and reports. · Data Sources: CRICS can ingest data from various sources using protocols like Kafka, REST, ODATA, and SOAP. · API Interactions: The system communicates through various API response formats like REST, WEBSOCKET, GRAPHQL, JSON, and STREAM In essence, CRICS leverages powerful AI to automate and enhance compliance processes, but it is explicitly designed with human stakeholders at critical interaction points, ensuring that the system remains accountable, adaptable, and aligned with regulatory and ethical guidelines..

[Audio] Let's quickly look at the CRICS proposed interface: The Overview Dashboard gives a snapshot of key metrics: identified risks, applied controls, overall risk score, and AML hits. Ingestion & Processing tracks data flow, highlights failures, shows processing modes, and details the file queue for easy troubleshooting. The Regulations Hub centralizes regulatory documents, showing details like source, dates, and changes for streamlined compliance management. A key feature is an ability to provide simple and understandable version of the language used in regulatory document and an AI-powered comparison of regulatory documents which quickly highlights the differences. The Risks & Issues Hub summarizes key risks by severity and area, helping prioritize and manage threats effectively. Jus to mention the enclosed mocks have been developed using Firebase Studio with technologies such as React, TypeScript, and JSON, etc. So, the benefits are clear: Proactive Risk Management, shifting from reactive to predictive. Increased Operational Efficiency through automation. Improved Compliance and Audit Readiness with traceable, AI-generated insights. Continuous Learning as our AI models adapt to new threats..

[Audio] Now, let's explore a use case related to a multinational bank's efforts to enhance its anti-money laundering (AML) compliance program through the implementation of the Compliance Risk Intelligence and Control System (CRICS). Key points include: The bank is responding to regulatory scrutiny due to failures in detecting suspicious transactions and delays in reporting. CRICS aims to improve detection systems, continuously monitor transactions for high-risk behaviors, and streamline compliance workflows. Challenges include outdated monitoring tools leading to false positives, excessive data influx, and siloed operations among teams. Advanced technologies such as data lakes, generative AI, and regulatory APIs will be utilized to consolidate data and automate processes. The initiative is expected to reduce false positives, expedite Suspicious Activity Reports (SAR) filing, and enhance overall AML effectiveness. Strategies to mitigate risks include using diverse training datasets, enforcing data privacy protocols, and employing generative AI for stress testing. Overall, the bank seeks to build a more robust AML framework that meets regulatory demands and adapts to an evolving financial landscape..

[Audio] There are few potential future cases which discusses the implementation of an AI-driven system to combat ghost borrowers—fictitious entities that secure loans without repayment intentions. Key objectives include enhancing credit risk scoring, proactively detecting fraud, and reducing financial losses. The system requires an existing credit framework, reliable data sources, and regulatory compliance. It involves multiple agents for data handling and decision-making, with strategies to mitigate bias and ensure data privacy. Overall, the initiative aims to strengthen the integrity of financial lending processes..

[Audio] So, now we arrive finally at the climax scene, and we are very excited about CRICS's potential to transform operational risk management and regulatory compliance, and I hope the preview gave you a clear understanding of its capabilities and benefits. We welcome any further feedback or questions you might have..

[Audio] The Appendix focuses on Generative AI (Gen-AI), which creates new content by recognizing patterns in existing data, enhancing creativity and automating processes. Key benefits include automating manual tasks, providing personalized experiences, and driving innovation..

[Audio] Guardian AI Dashboard: Aids in combating money laundering by generating alerts and improving compliance, despite a 35.7% false positive rate..

[Audio] Ghost Guard Dashboard: Targets ghost borrower fraud with a 92.0% detection rate, reducing financial impact and improving loan application tracking. Overall, the integration of Gen-AI into risk management showcases its transformative potential, making it essential for organizations focused on innovation and effective risk intelligence..

[Audio] Client: FOP: Alliances: Technologies:. Unlocking the Next Frontier in Risk Intelligence.