Internal Audit Data Analytics

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1 Internal Audit Data Analytics

2 What s driving the need for enhanced data analytics in the market Analytics maturity model Capabilities developed and adopted Capabilities used to drive audits Defined goals and standardized processes and tools Capabilities are well developed and practiced with appropriate governance Data sources are readily available Activities begin to become repeatable and CM metrics are developed Teams are 10% of department Scale is achieved for department specific teams Continuous improvement methodologies are implemented Continuous monitoring occurring for metrics and controls Insight Return on Inve estment, Effeciency, Limited capabilities Ad hoc activities resulting in unpredictable and inefficient performance Success based on individual competence Initial Developing Defined ed Advanced Industry Current State Leading Program Maturity August 14, 2012 PwC 2

3 Drivers and value of analytics within internal audit The risks highlighted as a result of the financial crisis has required organizations to develop a deeper understanding of the businesses they manage. They cannot simply rely on existing control structures. They must evaluate transactional activities for patterns,trends and anomalous behavior. One of the most efficient i ways to develop this understanding di and test t these transactions ti is by developing sound information management practices and conducting forensic testing. Drivers What is creating demand for analytics? Regulatory expectations to monitor business activities are increasing. Value Deeper business understanding & focus on risk Why is it so important for organizations to get it right? Analytics allows specialists to link data to business processes allowing us develop a better understand of the business and associated risks. Data is growing exponentially, while the skill sets to use tools beyond access and excel is decreasing. Lower existing and future costs Advanced analytical techniques are more cost efficient than traditional methods. A recent study indicated that an analytical procedure costing $0.01 could cost as much as $4 to perform similar procedures using traditional testing. Organizations are being forced to do more with less. End-to-end testing Analytics can track data across functions and units allowing us to identify issues missed by traditional (more focused) testing. Competitive pressures are forcing organizations to innovate. Key technology, business and data hybrid skills are in high demand. Increased population and control coverage Real-time evaluation of controls and data integrity - facilitates what-if analysis Analytics allows for 100% population testing as compared to traditional sample testing. In addition, analytics allows us to reverse engineer issues identified during testing. Clients can view actual mistakes in logic or processing errors rather than focus on hypothetical risks this can accelerate root cause analysis and assist in the implementation of new controls. August 14, 2012 PwC 3

4 Value of data analytics Audit execution Continuous Monitoring Finding Follow Up Risk Assessment Reporting Audit Execution Analytics within Audits will be focused on techniques to test the operating and design effectiveness of our control environment. The goal of the procedures outlined below will remain focused on allowing IA to give stakeholders recommendations on their control environments. Area Type Description Examples Business Analytics Profiling Data Mining Analytics used to identify business control weaknesses and recommend design enhancements Data Governance Analytics Analytics used to test IT controls within technology systems Statistically profiling the quantitative and qualitative parameters of a set of data to identify risk areas (anomalies, spikes, unusual patterns) within a business and/or process. Using analytics to mine payments data to identify systems to focus on and identify patterns of money movements in high risk countries. Stratified Conduct risk based analysis of data and target portions of Using analytics to choose sample of Sampling sampling on higher risk dimensions of business areas. surveillance exceptions based on volume and value of trades, high volatility trading days, traders with the highest level of exceptions. Logic Recreation/ Recalculation Reconciliations Data Quality Utilizing business requirements or independent sources of data to recreate selected portions of the logic of data based control processes (reports, business processes). Comparing sets of data between hand off s and/or independent sources to measure completeness and accuracy. Examining completeness, validity, consistency, timeliness and accuracy of enterprise data as it moves from source to reporting. Using analytics to re-perform risk calculations performed by vendor systems which aggregate data from multiple sources. Using analytics to compare data sent to regulators in reports vs. source systems. Using analytics to isolate data quality issues within source systems which may cause downstream report breaks. August 14, 2012 PwC 4

5 Value of data analytics Continuous monitoring Continuous Monitoring Finding Follow Up Risk Assessment Reporting Audit Execution Analytics within continuous monitoring will be used to assist in the analysis of identifying risk metrics to be used to quantitatively compare and cross-rough risk in our audit universe. The outline below is a potential framework. Framework Development Currently the department engages in a qualitative process to assess risk as part of our continuous monitoring program. Core Technology Team The potential natural evolution of the program is to add a quantitative component that would drive Internal Audit Analytics Team risk analysis and comparison. To achieve this a framework for development would CM Working Group not need to be agreed upon and built. The following is one proposal for a framework. Pilot Metrics Development Once agreement and management support on a framework is developed analytics intelligence will work Internal Audit Analytics Team with business and technology audit teams to begin piloting and conduct behavioral analysis on data Business and Technology within the firm to assist in creating meaningful, Auditors impactful, risk based metrics. CM Working Group Core Data Sources Aggregation, comparison and analysis Metrics and interactive dashboards A proposed framework for continuous monitoring is to develop a technology environment where selected data can be ingested, pre-defined analysis can be conducted and interactive dashboards can be produced with metrics. The dashboards would allow auditors to filter and compare different dimensions of the metrics to gain a higher level or lower level analysis to gain insights providing our team the ability to conduct what if and drill down analysis to drive unique conclusions. OU 1 Trading Desk 1 PWM Office 1 OU 2 Trading Desk 2 PWM Office 2 OU 3 Trading Desk 3 PWM Office 3 Comparable Entities Aggregation, Comparison and Analysis Production Roll Out Core Technology Team Once a metrics and a process to develop a metric has been approved by the supporting OU owners, IA analytics will working with core technology to put the metric and corresponding dashboards into production for continuous monitoring use. August 14, 2012 PwC 5

6 The following case studies demonstrate practical examples of analytics work performed and the value driven by the analysis. Liquidity Risk Regulatory Reporting Challenge: Determine if any unusual variances exist in two months worth of 4G reports, consisting of 523,000 individual data points spread throughout 40 different excel spreadsheets. Analytics Leveraged: Visual Analytics, Data Profiling Tools: Spotfire, SQL Server with BIDS Bank Regulatory Reporting Challenge: Report logic specifications were incomplete and required significant data transformation, increasing the complexity of report re-performance and root cause analysis of discrepancies. Analytics Leveraged: Re-performance Tools: SQL Server with BIDS Benefit Benefit Leveraging visual analytics significantly ifi accelerated data Leveraging SQL Server with BIDS enabled a the development ingestion and aggregate analysis to identify significant of a logical process flow, accelerating logic development and variances over time for sample selection and review. simplifying root cause analysis. Time Savings*: 120 hours Time Savings*: 80 hours Assurance: 100% population coverage, 2 months Assurance: 100% population coverage, 1 day August 14, 2012 PwC 6

7 Internal Audit analytics maturity model PWC uses the Maturity Model below to assess Internal Audit Analytics performance and identify priorities for improvement opportunities Below industry practices At industry practices Leading practices y Strateg Priorities dictated by surprises Reactive to business needs Limited measurement of processes, etc. Priorities dictated by immediate business needs Moderate anticipation of analytics needs Limited measurement of processes, targets not set Clearly defined strategy and roadmap Metrics in place to track performance Integrated on a global basis Key Component ts of a Internal Aud dit Analytics People Organization/ structure Process Techn nology Risk Assessment Audit lanning Controls Testing P Duplicated & fragmented Unclear roles and responsibilities Small span of control Narrow skill base Limited development; ad-hoc training Limited analytics understanding Limited integration / no data governance Manual data collection processes Extensive Excel-based Access ACL Analytics Fragmented & non-standardized Process inefficiency & duplication Business criticality drives process evolution Ratings defined through interviews Limited on-going analysis Audit Universe only qualitatively analyzed No formal measurement strategy Limited use of metrics Manual data acquisition Organizational structure overlaps Roles and responsibilities understood but not documented Moderate span of control Narrow skill base Functional training and development Uneven expertise across functional audit areas Multiple and redundant systems and data feeds Limited data acquisition automation Limited advanced tools and information sharing Minimal data governance Optimized by function and audit area Quality Control Processes Process inefficiency and duplication Minimal use of workflow and mapping tools Metrics from business leveraged Some on-going analysis Universe linked to profit and cost centers Centers of Excellence leveraged Integrated coordination & management Roles and responsibilities clearly defined Strong analytics mindset staff rotations & leadership programs Deep technical skills & understanding Automatic data capture Strong data governance Analytics specific tools and data warehouses End-to-end process ownership and documentation Standardized process flows and leverage of workflow and mapping tools Independent Risk Metrics On-going Risk Analysis Shared dashboards Scope defined through interviews Some data utilized to understand business Data used for all applicable areas of business Data not leveraged to make scope decisions Data used to guide scope discussions Excel and ACL modeling Outside metrics leveraged for scoping Shared dashboards Analytics use driven by requests Most Analytics used for reconciliations No historical testing libraries Analytics planed quarter by quarter Multiple analytics techniques utilized Some historical work papers Analytics discussion as part of annual planning Spread of multiple techniques Continuo ous Monitorin ng Monitoring defined through interviews Limited on-going analysis Manual processes Metrics from business leveraged Some on-going analysis Excel trending of metrics Independent analysis and metrics Shared dashboards Use of external data PwC 7

8 Appendix D: Case study Internal Audit analytics August 14, 2012 PwC 8

9 AML Internal Audit analytics Internal Audit analytics Analytics is a powerful auditing tool given the significant dependency controls have on data. Business-driven controls: Technology-driven di controls: Risk Assessment Customer Risk Scoring Processes s Know Your Customer (KYC) Investigations OFAC/Watch List Screening Transaction Monitoring SAR Filing SAR Reporting Transactional Data Da ata Entity Data Other Referential Data (Alerts, SARs, Watch Lists, Risk Scores, etc.) Analytics enables a bottom-up approach to reviewing and evaluating the design and operating effectiveness of AML- related control environments by leveraging data. ACAMS PwC March

10 AML Internal Audit analytics Auditing life cycle Analytics can be leveraged as a tool within each phase of the AML auditing lifecycle. 1 Risk Assessment Assessing transaction/business risk Assessing customer/counterparty risk Analytics enables regular, periodic reporting and accelerates on-going risk identification and assessment. Auditing Life Cycle Analytics increases assurance through improved population coverage and more intelligent, risk-based sample selection. 3 Continuous Monitoring Metrics and reporting (alerts, SARs, etc) Business/product/behavioral changes Audit Testing Transaction surveillance re-performance Below-the-line threshold testing 2 ACAMS PwC March

11 AML Internal Audit analytics Auditing life cycle Risk assessment The following highlights the benefits of analytics applications within AML risk assessments. Objectives I. Identify measurable risks II. Evaluate critical controls III. Refine audit plan IV.Define audit scope (high risk areas) Sample activities Assess business risk - Measure activity (transactions, accounts, customers) by line of business - Identify anomalies Assess customer risk - Measure behavioral profiles (volumes and values) - Relate customers to risk factors (geographies, products, etc.) Techniques/enablers Profiling Data Mining Statistically profiling the quantitative and qualitative parameters of a data set to identify risk areas (anomalies, spikes, unusual patterns) within a business and/or process. ACAMS PwC March

12 AML Internal Audit analytics Auditing life cycle Audit testing The following highlights the benefits of analytics applications within AML audit testing. Objectives I. Increase testing effectiveness II. Increase assurance (through increases in population coverage) III. Identify the needle in the haystack IV.Validate data conditions (completeness/quality) Sample activities Test screening effectiveness - Leverage fuzzy matching algorithms to mimic watch list screening - Stratify and sample matches at varying levels of similarity Below-the-line threshold testing - Model scenarios against surveillance logic - Identify and sample activity just below alert threshold values ACAMS PwC Techniques/enablers Logical Recreation Independently develop logic and execute with actual data to identify implementation gaps against requirements Data Quality Test actual data against quality checks to evaluate data conditions which may impact control effectiveness Stratified Sampling Profile data to identify higher risk transactions or events for sample selection Identify near-miss alerts and conduct sample reviews to evaluate risk. March

13 AML Internal Audit analytics Auditing life cycle Continuous monitoring The following highlights the benefits of analytics applications within continuous monitoring. Objectives I. Improve availability and frequency of information II. Understand and react to changes in risk profiles III. Leverage prior audit work or risk profiling for goforward reporting IV.Forecast future risk areas Techniques/enablers Reporting Providing on-demand or high frequency key risk indicators for continuously monitoring risk profiles Predictive Analysis Leverage current trends to forecast future results and assess what-if scenarios Sample activities Transaction monitoring dashboards - Monitor changes in transactional activity - Measure ongoing transaction monitoring alerts and SARs by scenario Capacity forecasts - Estimate future alert volumes given observed activity trends and current output ACAMS PwC March

14 AML Internal Audit analytics Data validation techniques Data validation is a fundamental component of effective AML control auditing. A Data completeness B Data quality C Referential integrity it Verify that the source data reconciles with an independent system of record. Example: Source file control totals (i.e. record counts) reconcile with volumes from the control file (i.e. system of record) or tie back to management reporting. Verify that elements critical for surveillance are in line with expected data conditions (standardized formats or value ranges). Example: Validate values of transaction amounts and dates. Validate availability of counterparty fields (no null or blank values). Verify that relationships between core data (transactions and customers) accurately and completely map to referential data (risk scores, jurisdictions). Example: Validate jurisdictions exist for all counterparties in transactional data. Validate risk scores are available for all customers. ACAMS PwC March

15 AML Internal Audit analytics Sample testing approach The following is an example of the application of analytics techniques within an AML audit. Test step Results 1 Validate data completeness Wire data in the compliance data mart was reconciled against feeds extracted from Treasury payment processing systems. 2 Validate quality of source data Customer addresses were analyzed to identify state and country values out of range or in an inappropriate format (i.e. Canada rather than expected CA abbreviation). 3 Validate implementation of surveillance detection logic 4 Validate the productivity of surveillance results 5 Assess risk of targeted surveillance typologies Surveillance scenarios selected for testing were modeled in an analytics platform (recreating each of the logical conditions defined within system requirements) and the results reconciled. Alert data was profiled to identify areas of high productivity and low productivity to assess effectiveness of coverage. Raw transactional data was quantitatively modeled to identify potentially suspicious, i outlying activity it and vetted against actual alerts. ACAMS PwC March

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