Forecasting, Data Mining and Anti-Money Laundering
~ Cleaning up with data warehouse analytics
After the first dot-com wave receded, a new information revolution followed. Technology that has enabled the world to connect online has intensified competition. Now the winners are those companies that invest in solutions that can identify patterns, exposures and opportunities in their massive storehouses of data. For example, instead of investing in new buildings and high-ticket entertainment, Harrah’s invested in understanding their customers better – and became the world’s largest gaming company. Another example, Australia’s BankWest, leveraged its Basel 2 compliance initiative to identify the early signs of customer distress (and possible default on loans).
The research study, Competing on Analytics, by Harvard Business School, was based on discussions with executives and directors at more than thirty industry-leading and globally competitive organizations. Thomas Davenport from Harvard Business School, cited organizations such as Capital One, Harrah’s Entertainment, Progressive Insurance, Marriott, Procter & Gamble, Wal-Mart, and sports teams such as the New England Patriots and Oakland Athletics, as true “analytical competitors” that have excelled through a greater reliance on analytic processes and technologies. Davenport says. “The executives we questioned were clearly interested in identifying the best strategies for organizing analytic operations on an enterprise scale. They’re really taking this seriously….(and for) an increasing number of companies, these activities have moved from the margins to the mainstream. For many, the use of analytics has become a primary activity used to support the overall business strategy. ”
This presentation will drill into three unique applications of Data Warehouse analytics: Forecasting, Data Mining, and Anti-Money Laundering:
- Each application leverages information to maximize the bottom line impacts – which can exceed 10’s of millions of dollars
- Each requires specific data transformation down to the lowest level e.g. customer, service, transaction or SKU within the store
- Each relies on automation to enable scalability
- Each provide a competitive advantageous built on data warehousing infrastructures
We will cover off the barriers that have been overcome, how benefits are realized, and how the automation is scalable.
Craig Carothers is a SAS Business Solution Consultant with over 20 years of experience. His skills include: Forecasting and Budgeting, Revenue Optimization, Data Mining, Risk Management, Distribution planning, CRM and application design, addressing the needs for the Financial Services, Energy, Communication, Retail, and Manufacturing Sectors.