www.pwc.com Finding the Sweet Spot Using analytics to combine Fraud and AML October, 2012
Overview Who are we? John Sabatini Partner PwC John.a.sabatini@us.pwc.com Vikas Agarwal Managing Director PwC Vikas.k.Agarwal@us.pwc.com What are we going to talk about today? Why combine AML and Fraud Where is the Industry What are the challenges What are core elements of a successful program? What are the key? benefits Key Elements for Convergence Applying Analytics Case Study PwC 2
Why Combine AML and Fraud Drivers Value What is creating demand for convergence Why is it so important for organizations to get it right? Regulatory expectations to look across the business and create FIU s are increasing Typologies used by Fraud and AML likely individuals are trending to be more in common Organizations are being forced to do more with less and reduce compliance costs Deeper understanding of your customers Lower existing and future costs Target the right alerts Convergence allows organizations to think more holistically about the customer and combine different elements of data Convergence not only help reduce costs of investigations but reduces the cost of managing technology Convergence helps investigators conduct more meaningful investigations Competitive pressures are forcing organizations to innovate. Regulators are looking to breakdown silo s Regulatory are looking for organization to converge towards enterprise views Data is become difficult to manage in silo s Businesses are growing and become more complex Organizations that are growing must find quicker ways to integrate regulatory expectations in M&A activity August 14, 2012 PwC 3
Based on the path set by the Financial Services industry organizations are beginning to move up maturity curve Return on Investment, Effeciency, Insight Limited capabilities Ad hoc activities resulting in unpredictable and inefficient performance Success based on individual competence Initial Developing Capabilities developed and adopted Capabilities used to drive risk program Defined goals and standardized processes and tools Defined Capabilities are well developed and practiced with appropriate governance Data sources are readily available Activities begin to become repeatable and metrics are developed Advanced FS Industry Current State Scale is achieved for department specific teams Enterprise case management combining data sources Program Maturity August 14, 2012 PwC 4 Leading
What are the challenges of combing Fraud and AML? Difficulty scoping and developing procedures Do we have the right people? How do we begin? Experienced resources can be difficult to find, and may not be available when needed Analysis may have the technical skills but lack industry, business process, issue or other experience Lack of repeatable processes may cause analysts to re-invent the wheel Inefficient data collection process Are we getting the right data in an efficient and timely manner? Data acquisition is often time-consuming and can cause delays and multiple false starts Lack of data control causes difficulties in setting and delivering to expectations ERP configurations, niche applications and legacy systems complicate mapping and collection Inability to ascertain the quality and reliability of the data itself causes uncertainty Difficulty interpreting and filtering findings How do we truly combine scenarios and alerts? Convergence must have the insight to interpret results, refine analysis and reduce false positives Convergence requiring trending, forecast and predictive modeling require greater interpretative skills Reliance on ineffective or inappropriate analytic tools can limit insights and mask findings Unclear return of investment Did the data analytics program yield the expected benefits? Convergence requires clear objectives which increase control, generate insights, forecast or predict Overall analysis processes must be effectively managed Convergence must keep with the plan and resist getting lost in the details August 14, 2012 PwC 5
Core elements of a successful AML/Fraud Convergence program Strategy Well defined mission, goals and vision, 90 day, 1 year and 3 year roadmap with milestones Measurable metrics Governance Global integration Strategy Structure Structure Definition centralized team, steering committees, and roll of department in execution of analytics Center of excellence Integration and Coordination Defined rolls and responsibilities across organization Technology People Technology Infrastructure, tools, and process to support core analytics, information sharing and data access Data-warehouse Automated data capture Information security processes Rationalized vendor portfolio Visual analytics tools Sharing capabilities Process Process Defined methodology, quality control, and work papers to support various audit analytics Standardized process Workflow and mapping tools People Programs for cultural mindset shift including executive sponsorship, training, and incentives Strong analytics /forensic mindset Staff and leadership program On-going structured and unstructured training Deep technical skills and understanding August 14, 2012 PwC 6
When executed diligently and systematically the organization will see benefits directly Quantifiable Benefits Reduced portfolio maintenance and vendor spend Eliminate redundancy in vendor tools, eliminate vendor overlaps, and rationalize vendors Engage vendors for optimized software licensing agreements Simplified technology and realigned FTE count Consolidate skill sets around fewer standard technologies to support and to maintain Develop deep expertise within a smaller technology set Remove overlapping complexities Focus FTEs away from overall portfolio maintenance portfolio and onto delivering market innovation Reduced development costs & delivery time Reuse common functional services to reduce custom development on a per-project basis Qualitative Benefits Improved service levels and responsiveness Meet planned demand growth through improved scalability, performance and application resilience (i.e., turn-on additional capacity when needed Increase agility to response to changing market and regulatory demands Builds compliance processes into reusable services to reduce time and cost of compliance Simplified system architecture and increased enterprise-wide data sharing Increase end user productivity through increased enterprise information sharing enabled by breaking down information stovepipes Improve data accuracy through application of consistent business rules and encapsulation of processes across the enterprise PwC August 14, 2012 7
Key Elements for Convergence KYC AML Transaction Monitoring Social Media Fraud Transaction Monitoring CTR Watch Lists SAR Source Data External Lists PwC 8
Applying Analytics 1 Data Mapping Utilize analytics to combine data between systems into a common data model 2 Tier 2: Patterns and Indicators Utilize analytics to look for trends between and matching between scenarios 3 Alert Consolidation Utilize analytics to consolidate alerts for more effective investigations 4 Holistic View Utilize analytics to consolidate views of alerts and SAR effectiveness ratios PwC
Case Study Homer Simpson Checking (joint a/c with Marge) CheckX Marge Simpson Checking Chexck Moe s Tavern LLC Business Operations Commercial X Joe Quimby Checking CheckX2 Kwik-E-Mart LLC Business Operations SmallBusX C. Montgomery Burns Investment Trust Vehicle PrivateX Trust Signer Springfield Nuclear Power Plant CFO CEO Chairman Payroll Clearing and Settlements Business X August 14, 2012 10 PwC
Before Account Alerts AML Investigation Time Alerts Fraud Investigation Time Total Cost -Unknown source of wealth 1 hr -Bounced checks - Large number of checks from elderly 1hr 2hr -Velocity of funds - Structuring 2 hr -Missed credit card payments - out of country credit card payments 2hr 4hr -high risk customer -PEP - Negative news 3hr None 3hr -structuring -high risk country payments 1.5hr - Duplicate RDC checks 5 hr 6.5hr PwC
After One team conducting investigation Alerts consolidated to identify patterns New insights and networks Before After Savings 15.5 Hours 5.5 Hours 64% PwC
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