Fraud Trend Analytics

To understand your organization’s opportunities to capitalize on analytics to drive insight, innovation, and competitive advantage, it helps to begin with the current state of the field. Analytics is a constantly evolving science that has changed dramatically over the years and continues to advance rapidly today. Analytics now spans five categories: Descriptive, Diagnostic, Predictive, Prescriptive, and Cognitive. These categories build on each other in a stepwise manner. Each step along this path moves the business toward an on-demand enterprise, where decision making becomes smarter and faster. It is important to understand where you are on the maturity path so you have a clear perspective on your current capabilities and where you want to go from there.

“How can I use data analytics solutions to create value for my organization?”

1. Descriptive Analytics – Hindsight (What happened?)
2. Diagnostics Analytics – Oversight (What is happening? Why did it happen?)
3.  Predictive Analytics – Foresight (What will happen?)
4. Prescriptive Analytics – Insight (How can we optimize what happens?)
5.   Cognitive Analytics – Right Sight (What is the right action, decision for now?)

Descriptive Analysis

Descriptive analysis is an insight into the past. This statistical technique does exactly what the name suggests -“Describe”. It looks at data and analyses past events and situations for getting an idea of how to approach the future.

Examples:

• Summarizing past events such as regional sales, customer attrition, or success of marketing campaigns
• Tabulation of social metrics such as Facebook likes, Tweets, or followers
• Reporting of trends (year on year, month on month)

1. Descriptive Analytics – Hindsight(What happened?)
2. Diagnostics Analytics – Oversight (What is happening? Why did it happen?)
3.  Predictive Analytics – Foresight (What will happen?)
4. Prescriptive Analytics – Insight (How can we optimize what happens?)
5.   Cognitive Analytics – Right Sight (What is the right action, decision for now?)

Diagnostics Analysis

The functions of diagnostic analytics fall broadly into three categories:

Identify anomalies: Based on the results of descriptive analysis, analysts must identify areas that require further study because they raise questions that cannot be answered simply by looking at the data. These could include questions like why sales have increased in a region where there was no change in marketing, or why there was a sudden change in traffic to a website without an obvious cause.

Drill into the analytics (discovery): Analysts must identify the data sources that will help them explain these anomalies. Often, this step requires analysts to look for patterns outside the existing data sets, and it might require pulling in data from external sources to identify correlations and determine if any of them are causal in nature.

Determine causal relationships: Hidden relationships are uncovered by looking at events that might have resulted in the identified anomalies. Probability theory, regression analysis, filtering, and time-series data analytics can all be useful for uncovering hidden stories in the data.

We help you convert your data into easy to read, visual-pro form so that you can start to explore data to gain insights that can be shared in a simplistic manner with your teams. We adopt the approach of Understand, Adapt and Transform; ensuring that all the solutions we deliver, creates efficient and accurate decision making process to positively impact your business.

Data cleansing is the process of ensuring that your data is correct, consistent and useable by identifying any errors or corruptions in the data, correcting or deleting them, or manually processing them as needed to prevent the error from happening again. It is critical to ensure your data is in its pink of health before any system migration or implementation.

We provide the diagnostic analytics approach for enterprise-wide fraud detection, prevention and management. We apply your business rules to sift out the anomalies, linking internal and external data and scaling to voluminous data. For example, intentional double payments, creation of phantom employees and getting paid, unauthorized access rights given and resulted in unapproved funds or stock transfer.