eDelta assists law firms, major corporations, electronic evidence discovery (EED) companies and government entities in various aspects of the litigation data cycle. Our team provides data retrieval/collection, restoration, analysis and reporting services – all with forensic-level, expert witness quality.
Highlights of this service offering includes:
Data collection – forensic quality collection of hard drives, media, etc.
Data restoration – emails and other archived information can be retrieved from almost any format
Data mining and extraction – identification of important financial or similar data, critical for court documentation
Data analysis is the process of developing customized tactical solutions to validate the accuracy and completeness of information. eDelta helps analyze information in drastically shorter time frames not only by bringing the right expertise to the project, but also through the use of data analysis tools, including SAS and ACL. We help analyze raw information, assist in due diligence processes and provide decision support to our clients.
In addition to litigation support, high-quality data analysis supports key business areas, such as:
- Internal/external audit – as part of financial audit support we develop standard Computer Assisted
- Audit Techniques (CAATS) to assist in risk assessments
- Process improvement (automation) – businesses can implement automated IS solutions which
- minimize the risk of human input error and reduce cost by decreasing data processing time and
- increasing efficiency
Examples of decision support and compliance in the banking industry would include:
- Identify profitable/unprofitable loans – identify loans that are over or under specified amounts,
- terms, or interest rates
- Loan interest calculations – verify loan interest is calculated correctly and properly reflects payments
- of principle, contracted rates and periods
- Loan losses – analyze impact of past-due loans, unearned income, accrued interest
- Generate key financial reports to help identify trends in data