AI: A risk and way to manage risk
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The document discusses the intersection of artificial intelligence (AI) and risk management, highlighting how AI can transform risk management from a cost center to a profit center through predictive analytics and data science. It outlines challenges such as algorithmic bias, legal risks, and data quality issues while emphasizing the importance of a robust data strategy and integrated platforms for effective risk management. Additionally, the document details best practices and successful implementations of AI-driven solutions in risk management for financial institutions.
AI: A risk and way to manage risk
- 1. Karan Sachdeva IBM Asia Pacific [email protected] M- +65 9028 3694 AI: A risk and way to manage risk
- 2. Digital Risk: Where There is Money, There is Risk “Opportunity and risk come in pairs” Bangambiki Habyarimana, The Great Pearl of Wisdom
- 3. risk manŸageŸment noun (in business) the forecasting and evaluation of financial risks together with the identification of procedures to avoid or minimize their impact What is Risk? artificial intelligence noun Understand Reason Learn Interact
- 4. Risk Management Scenarios addressed with AI and Data science Predictive Analytics Anomaly Detection “I don’t know what to measure”“Here’s how big my problem is” Applications- Credit Risk Transaction fraud Identity theft Insurance claims Applications- Rogue trading Money laundering Terrorist financing Compliance Anomaly detectionPredictive Fraud Analytics Data Science = Applied AI
- 5. Risks with AI and Data Science 1. Algorithmic bias 2. Data Quality Issues 3. Programmatic errors 4. Risk of cyber attacks 5. Legal risks and liabilities 6. Reputational risks
- 6. Data is the Primary Resource for Risk Management Customer Insight Compliance is mandatory for any data strategy Data Science and AI can transforms risk from a cost center into a profit center and enables immediate rather than staged benefits.Cost Savings Compliance Competitive Advantage
- 7. 7 Risk Management Challenges are compounded by the ever increasing volume of data and the need for AI of data is either inaccessible, untrusted or unanalyzed80% of data scientists’ time is productively utilized – rest is spent finding, cleaning, organizing data20% only AI Create a trusted analytics foundation COLLECT Make data simple & accessible ORGANIZE ANALYZE AUTOMATE Scale insights on demand TRUST Achieve trust & transparency Apply ML everywhere of enterprises do not yet understand the data required for AI algorithms 81% IBM Cloud / © 2018 IBM Corporation
- 8. Top 5 Best Practices to manage risk with AI and Data Science 2. Getting the foundation right- Single Integrated Data Platform 3. People: Data Engineers, Data Scientists and Business Executives 4. Defining ROI and charging back 5. Trust and Ethics- Deliver in constraints of regulatory pressures and data privacy. 1. Identifying risk areas and business problem
- 9. 1. Use Case Generation and Prioritization
- 10. 2. Integrated Modern Data Platform- IBM Cloud Private for Data IBM Cloud Private for Data (Multi-Cloud) Business Users & Analysts Data Engineers App Developers Data Scientists Data Stewards Custom Extensions Enterprise Cloud Microservices Containerized Workloads Multi-Cloud Provisioning Data & AI Microservices Analyze Data Trust AI Infuse AIOrganize DataCollect Data 10
- 11. 11 3. Get the people equation right Architects data pipelines and ensures operability Gets deep into the data to draw insights for the business Works with data to apply insights to business strategy Plugs into analysis and code to build apps DEPLOY COLLECT Data Engineer Data Scientist Business Analyst App Developer Governs data and ensures regulatory compliance Data Steward CXO Sys Admin Access data Transform: cleanse Create and build model Evaluate Deliver and deploy model Communicate results Understand problem and domain Explore and understand data Transform: shape ANALYZE ORGANIZE
- 12. 5 X R O I 4. ROI- Much More then $$$
- 13. 13 Manage fluid data with built-in protection and compliance (e.g., GDPR) Profile, cleanse, integrate and catalog all types of data AI-based Metadata Management and Data Lineage Persona-based experiences with built-in industry models Govern data lakes and data warehousing offloading 5. Trust & Ethics Create a trusted, business-ready analytics foundation Containerized Integrated End to End Analytics Platform Seamless hybrid and - multi-cloud support Ethical and Trusted Data IBM Cloud Private for Data Policy and business driven visibility, discovery and reporting
- 14. Benefits of choosing IBM Cloud Private for Data based architecture for Risk Management 1) Big Data: Wide velocity, volume and variety of fraud-based data from multiple sources; 2) Faster: Automates labor-heavy fraud data tasks, such as data preparation and organization; 3) Easier: Easily create & test the best-fitting anti-fraud data science models. 4) Secure: Robust data governance and metadata management capabilities for AI model inbuilt.. 14
- 15. 15 One of largest bank in APAC required centralized risk management intelligence to enable proactive identification, validation, and management of risk across a broad array of retail portfolios. IBM delivered a comprehensive set of risk management information requirements – including standard and custom risk and finance metrics. The system delivers centralized analytical capabilities, ad-hoc reporting, and dashboards modeled on the risk management value chain. Benefits § Centralized and efficient risk analysis, intelligence, and reporting § Integration with Basel II data, portfolio segmentation, and Economic Capital inputs in addition to traditional and other emerging risk metrics. § Capability for broad, deep, and reliable view of risk from many perspectives § Scalable and extendable to meet emerging business needs “IBM delivered the expertise, sense of urgency and collaborative approach required to design, develop and validate the Risk MI Platform. IBM neatly integrated into our business and technology teams. The combination of IBM leadership, business, technical and collaborative skills were key to our success in articulating the vision, delivering on the promise and easing the transition aspects of moving to a new enterprise platform.” — VP of Retail Credit Risk Improvements In Risk Management Intelligence And Integration Of Risk And Finance Challenge Solution Top Global Bank- Risk Management Transformation using Data Science
- 16. IBM industry leadership The Forrester Wave Predictive Analytics & Machine Learning The Forrester Wave Machine Learning Data Catalogs The Forrester Wave Conversational Computing Platforms IBM IBM 16 IBM #1 in AI Market Share Industry Design Awards Reddot Design Awards IBMIBM
- 17. Engage experts to monetize your data and get results in less then 4 weeks IBM’s Data Science Elite team IBM Cloud Private Experiences What do we offer? ü Free 14 days Sandbox for IBM Cloud Private for Data. ü Experience a 20 minute guided journey to build AI- powered applications ü Schedule 30 mins expert consultation ibm.biz/experienceICP4D Ibm.com/analytics/expert-advice Join us at APAC AI Council An exclusive community of like minded business and technology leaders to be the first to learn about a new ideas in AI, ML and data science space https://goo.gl/forms/Z4funOJnWf6OFKHz2 What do we offer? ü Free onsite engagement ü Identify use case(s) & Minimal Viable Products via discovery & design workshops ü Collaboratively build & evaluate data science and machine learning models ü Mentor & enable client teams hands-on www.ibm.com/analytics/ globalelite/ibm-analytics-data-science-elite-team
- 18. 20 18 Karan Sachdeva IBM Asia Pacific [email protected] M- +65 9028 3694