An example of the sorts of tools we employ in SEEC Innovation projects is the SEEC Forecasting Engine, which combines econometric forecasting tools with automation to enable objective forecasting of hundreds or thousands of data series. A challenge for most companies is determining how a market will perform over time, and how those changes will affect costs and revenues. With a rigorous data-driven approach leveraging internal and public data assets, the SEEC forecasting engine can power empirically grounded decisions and provide insight in periods of disruptive change. Further, these statistical forecasts can be integrated with automated tools that monitor results, changes, and shifts in opportunities to enable more responsive business and customer relationship management.
When building Forecasting Engine applications, SEEC first evaluates the data to be forecast and the end application these forecasts will power. Based on that evaluation, SEEC applies a battery of different statistical models that are potentially suitable for forecasting the data and for powering the end application. Forecasting methods are not all alike – finding the forecasting method that is the best for a particular data set and application requires an understanding of how models can break under certain conditions. SEEC’s forecasting approach evaluates the performance of every model against actual outcomes, and based on that (and other criteria) selects the best forecasting method for each data series.
The forecast that our algorithms select as optimal is ultimately integrated into a software application. This can take relatively simple forms (such as reporting of forecasts) and/or relatively complex forms (such as automated alerting when the application detects changes in fundamental market dynamics). The unique value of SEEC's approach is reliable forecasting methodology, integrated within a solution that enables the delivery of data at the right time with the right visibility, providing an organization with the ability to react and drive its business in the most effective method possible.
In SEEC's data-driven decision-making business, we recently completed a project to establish the price for a newly-developed fraud prevention service in the payments industry. In the course of that project, SEEC evaluated potential revenue streams between different levels of the broad payments ecosystem, delivering an integrated analysis taking into account the different types of players involved in payments and fraud prevention.
Pricing for fraud prevention involves credit card associations, issuers, merchants and consumers. Each of these stakeholders carries a different level of liability for fraudulent transactions depending on the type of purchase, the amount, the type of merchandise or service being purchased, and whether the card was physically present. Furthermore, the direct costs of fraud are not the only value at risk to stakeholders, nor necessarily the most important. In particular, an indirect cost of fraud prevention is erroneous purchase denials due to suspicions of fraud (false positives).
SEEC's pricing analysis took into account empirical measures of the opportunities and risks for each of the implicated stakeholders, using publicly available information, industry sources, and statistical computations to refine and aggregate the scale of the fraud prevention opportunity at every level.
Given these calculations of the extent to which different stakeholders stood to benefit from implementation of the new fraud prevention technology, SEEC developed an approach to pricing for the fraud prevention solution that would provide all of the relevant players with an incentive to adopt it.
Ultimately, we provided our client not only with a pricing strategy grounded in the facts of the market, but also with an understanding of how a complicated and previously unfamiliar market operates. Taken together, these enabled the client to confidently approach the complex negotiations involved in bringing their new service to market.