Kroll Ontrack Data Analytics. Forensic analysis and visualization of complex data sets to provide intelligence around investigations

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1 Trade Date: :57:00 Sum of Total Value: 287,663,728 Trade Date: :51:00 Trader Name: Susan Wright Sum of Total Value: 443,382,018 Kroll Ontrack Data Analytics Trade Date: :28:00 Sum of Total Value: 340,778,380 Trade Date and Symbol broken down by Trade Type. Colour shows details about Trader Name. Size shows sum of Total Value. Details Forensic analysis and visualization of complex data sets to provide intelligence around investigations

2 Kroll Ontrack Data Analytics Kroll Ontrack s Data Analytics team delivers forensic analysis and visualization of often large disparate sets of financial, operational and transactional data to provide intelligence around investigations. Specifically, we help with: Profiling and collecting of data sets Identifying, acquiring, and normalizing relevant data Identifying relationships between multiple data sources (in other words, understanding the database schema) Reviewing financial transactions to identify irregularities and red flags Data mining and manipulation Reporting and visualization of results Gaining intelligence from data analytics In litigation and investigations or Word documents are frequently collected, filtered and reviewed or produced to other parties. These typically exist as static and self-contained files which can be easily identified, searched across and admitted into evidence as standalone documents. A detailed analysis of complex data such as financial or transactional data can help unlock information that is relevant to an investigation or dispute. When this data is stored in complex, systems in relational databases that contain many parts, special tools and skills are required to identify it. Often databases are made up of thousands of different tables containing large volumes of information. When only a few specific tables are significant to an investigation or litigation case identifying the correct data is of paramount importance. It is also essential to extract it in a way that preserves its integrity, and analyse and present it to legal teams in meaningful ways. What if you could assess and prioritise what data is truly relevant when faced with complex litigation and investigations? you could carefully analyse data from a large number of data sources, including financial and operations data, to gain a stronger sense of the big picture in a case? you could see more than just the numbers when conducting an investigation, and by visualising transactional records gain insights into what has happened? you could show your findings using state-of-the-art visualisation software, arrange the results and present your information clearly and decisively in a way that brings the data to life? you could proactively sweep your systems to ensure compliance and operational protocols are strong and detect system breaches and red flags? What types of cases benefit from data analysis Financial or employee fraud Forensic investigations Insolvencies Valuation Disputes Due diligence investigation»» Investigation as part of regulatory compliance

3 The project lifecycle for data analytics Client Meeting: The critical first step is to understand your requirements and the context in which an analysis of data is to take place. It is also used to discuss the data infrastructure and identify internal stakeholders who will need to be involved. Data Scoping & Collection: Based on an understanding of your case, available data sources are assessed by experts who will help you identify what data should be collected for preservation purposes and analysis. Data is collected from a wide range of disparate sources: Financial databases (SAS, SAP, and PeopleSoft etc.) Platforms (SQL, DB2, Oracle etc.) Benefit: You gain access to information contained in complex data sets, including financial and operational data, expanding the scope of your investigative insight. Data Processing: Data is extracted from your system/s to Kroll Ontrack s systems for processing. Raw data extracted from different sources is organized into a common format so that it can be interrogated and analysed. Benefit: Unifying disparate sets into a common format allows you to adopt an integrated approach to data analysis. Data Analysis: Complex data cannot be loaded into a standard document review tool for analysis. Data experts therefore load the data into databases, work with your IT department to understand the data schema, run queries on the data and carry out raw data analysis. This involves: Identifying relationships between disparate data sources, including transactional records. Financial Transactional Review: Through forensic analysis of financial system data, we determine how specific financial activity occurred, discern relationships between custodians and help you better visualize transactional records. Data Mining and Manipulation: Examining data holistically to statistically determine any anomalies and detect any unusual patterns. Benefit: You are able to determine how specific activity occurred and identify potential evidence of fraud, corruption, non-compliance, etc. You can also identify trends, patterns or outliers in the data that might warrant further investigation Presentation to Clients: Analytics results are presented via visualization software in a graphical format to visually demonstrate findings. Benefit: Through easy-to-use visualizations, you are able to quickly gain insight from your data set, and visually identify anomalies or areas that need further analysis. NOTE: The process is highly iterative as clients provide feedback on initial findings and make additional requests based on the presentation provided, and Kroll Ontrack s data analysis experts carry out further analysis and present additional findings.

4 Graphical Interpretations of Data Large Value Traders Buy Trade Type Sell Trader Name Brett Granger Catherine Holt Trade Date: :22:00 Trader Name: Susan Wright Sum of Total Value: 607,783,298 Christopher Spitzer Eva Sanchez Ian Young Mark Thomas Trade Date: :57:00 Sum of Total Value: 287,663,728 Mary Souza Simon Bullard Stephen Armstrong Susan Wright Trade Date: :51:00 Trader Name: Susan Wright Sum of Total Value: 443,382,018 Trade Date: :28:00 Sum of Total Value: 340,778,380 Trade Date and Symbol broken down by Trade Type. Colour shows details about Trader Name. Size shows sum of Total Value. Details are shown for Total Value Timeline of GSIC Price Symbol GSIC Trader: Christopher Spitzer Price: 188,23 Trade Date: 14/08/ :22: Trader: Susan Wright Price: 188,11 Trade Date: 14/08/ :28: Trader: Christopher Spitzer Price: 180,87 Trade Date: 14/08/ :57: Announcement of Merger 155 Trader: Susan Wright Price: 180,88 Trade Date: 14/08/ :51: :37 11:52 12:07 12:22 12:37 12:52 13:07 13:22 Trade Date [14 August August 2013] 13:37 13:52 14:07 14:22 14:37 14:54

5 Our Experts Experience Our team members have experience working on the following types of matters: Fraud Investigations Trading Investigations: Working on high value fraud investigations involving asset tracing, system operation review and the reverse engineering of legacy trading systems to establish the flow of fraudulent transactions and activity across geographic locations. This can involve Consolidating different accounting systems and identifying trading discrepancies within them. Company Audits: Carrying out proactive audits for large trading companies such as energy companies. This includes examining many areas to guard against trading fraud including trade value, volume and value at risk limits, system integrity and escalation procedures, and areas of potential collusion among employees to circumvent trading limits. The data can be examined holistically to statistically determine any anomalies and detect any unusual patterns. Regulatory Compliance Sanctions Investigations: Working on an investigation into a global financial institution in relation to alleged sanctions violations. Interbank communication messages were extracted, processed and reviewed to identify transactions noncompliant with a sanctions list. Hundreds of millions messages, spanning multiple countries can be potentially relevant in these investigation. Messages can be screened using keywords in order to uncover suspect messages. They can also be linked to reconstruct transactions and all associated messages. Financial Restatement: Working with companies required by the SEC to restate financial statements due to errors in reporting. These projects can involve data extraction and consolidation of thousands of accounting records from disparate data sources, and the development of automatic and dynamic reconciliation processes. Corruption Investigations: Supporting investigations under the FCPA into contractors and suppliers in industries such as manufacturing. Sales records can be compared to data from accounting systems, inventory, shipping records, expenses and invoices to identify and reconcile discrepancies. Data can also be extracted from expense systems to assist in identifying large or unusual payments and to check whether appropriate authorisations have occurred. Customised solutions can be created to merge multiple databases into one interface. Forensic Investigations Internal Fraud: When companies suspect that they have been the victim of internal fraud data analysis can be relied on as part of the investigation that follows. Extractions of data from inventories, sales, accounting (SAP) and expense records of focused employees can be investigated in depth. Phone records, credit card purchases and s can be included in the investigation. Results can show how the fraud has been perpetrated, for example, a substantial increase in cost prices against the agreed contract price.

6 Due Diligence Customised Technology Solutions Internal Systems Review Corporate Acquisition: Due diligence investigations carried out in connection with the proposed acquisition of a company benefit from data analysis. A complete review of a company s accounting systems and controls can be undertaken. All historical data can be analyzed with a view to identifying any outstanding monies within the accounts payable and accounts receivable. Data Management System: Special solutions can be created to support business processes. For example, a data management system for loan valuations can be created for banks incorporating hundreds of data points from multiple data sources. Customised development can include automated reformation of source data, the integration and alignment of data points for each loan, the consolidation of loans for bundle assessments and the application of government regulated rates, processes and functionality. Financial Systems Review: Reviews of financial systems can be carried out to identify loop holes within the boundaries set by trading platforms, which may be triggered after trading limitations have been breached. OUR PEOPLE Kroll Ontrack Data Analytics (KODA) Services are performed by forensic data analysts with proven knowledge and methodologies to handle complex data sets including structured data. Our subject matter experts leverage deep industry experience and effective technologies to extract intelligence from data and present compelling visual findings. Martin Gawthorpe Director, European Digital Evidence Services Tony Dearsley Principal Consultant, Digital Evidence Services Damian Donovan Lead Consultant, Forensic Data Analytics Kroll Ontrack Nexus, 25 Farringdon Street London EC4A 4AB +44 (0) [email protected]