AUTOMATED INTELLIGENCE FOR M&A DUE DILIGENCE AND AUDIT

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Automated Intelligence for Due diligence and Audit

Saud MeethalDownload Article

Synopsis

Base Objective: Creating a connected analytics ecosystem for continuous operational and financial analysis of the organization which can be used for Due Diligence, Audit, Management etc.

The data that flows around in the formal and informal system whether digitized or not, can provide a plethora of actionable insights, if, the data is digitized, structured, cleansed and analyzed in an ideal manner. The whole process of automation of such data processing can enable the capabilities of assessing complete data and handling continual changes to requirements efficiently.

The process of due diligence and audit revolves around extracting summarized facts and insights from a set of similar data. Summarization and interpretation of this common data vary as per the requirements, which can lead to an M&A Transaction, Internal Audit, Forensic Analysis, basic MIS etc. But as there are certain commonalities in the parameters assessed, the base data preparation layer can be structured to suit all the possible requirements. More-over the structured data can be processed and synchronized with the data sources in real-time in many cases.

Structuring an Analytics Ecosystem: Could be generalized to, as per the domain. Implemented by consultancy firms.

Step 1: Getting the data right
The quality of the architecture of an organization’s operational and financial data stores has a direct impact on the ability to analyze and automate the data. Hence it is pivotal to digitize and process all the relevant data points into a logically structured data warehouse.

DS

AUTOMATED INTELLIGENCE

FOR M&A DUE DILIGENCE

AND AUDIT

PROFESSION OR INDUSTRY | LINK TO OTHER ONLINE PROPERTIES: PORTFOLIO/WEBSITE/BLOG

Step 2: Define the desired analysis

Incorporating the rules and regulations of the government, organization and considering other domain standards in the report design. Example: For Due Diligence of a microfinance firm we need to consider evergreening analysis, drop-out analysis, government rules and regulations as an integral part of the design template.

Step 3: Implementation

Automate using BI tools like Power BI, Tableau, Qlik View etc.
Create predictive analytics models using basic machine learning and time series analysis, wherever required.

Benefits of automated intelligence:

  • Quality of Due Diligence and Audit: As big data cannot be processed in excel, only a sample of data is analyzed. This may create an opinion of the data based on how good of a sample data is considered. But utilizing the complete population of data eliminates any chances of misinterpretation due to poor sampling.
  • Flexible and Efficient Change Request management: As the activity of analyzing is done on traditional tools like excel, which at times gets cumbersome, often the team needs to decide on what type of analysis they require and then build reports based on those points. With BI tool capabilities at their disposal, the team would have the flexibility to drill down the data from many more perspectives and combinations and create customized KPIs, which wasn’t feasible earlier due to limited time. This would also address any report changes due to the changes in the underlying data, in a very effective manner.
  • Recurring projects analyzed instantly: At times, the same team is required to carry out the Due Diligence or the Auditing activity. In such cases, depending on how well the data sources are synchronized during “Step 1”, the BI reports used earlier can simply be updated in an instant. The team would require working only on new requirements if any.
  • Replicable to multiple organizations: Consultancy firms can not only reutilize the BI architecture but also the reports and dashboards can be referenced for other projects belonging to the same domain. Domains here can be defined as categories like Manufacturing, Sales, Payroll, Micro Finance etc. The reports may not exactly be the same, but the scripts used to analyze can expedite the development time for the new project. Hence the more types of domains consultants address, the better the repository they create. This ultimately widens their spectrum of domain expertise by every project. The expertise can also be retained by the consultancy firm in the form of BI templates, even when the team has experienced some attrition.
  • Customized KPI for many other requirements: The generalized reports of any particular domain may cater to the majority of the requirements but may not always be sufficient. But as all the available data has been processed at “Step 1” it would not be a paramount task to cater to any specific analysis required by the stakeholders.
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Saud is an experienced Data Scientist with production environment exposure. He currently manage data science implementations in ERP System, Due Diligence, Fraud Analytics and Tax products. He have extensive knowledge of capturing data from different data sources from either On-Premise or Cloud environment. He also structure and store unstructured data from data-source-APIs, web scraping tools etc. or even archive data from images using OCR. From deliverables perspective, he have implemented various descriptive analytics projects using visualization tools and python scripts. He have also played a major role in developing and implementing a few predictive analytics projects. He has executed projects for remote clients based out in Myanmar, USA and various other domestic regions.

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