Fraud Detection / Protection from Industrial Espionage
Compliance has gained importance in all economic sectors. The analysis of internal data can help companies in the early detection fraud, corruption, or any other behavior that could damage the reputation of the company. Especially in Germany, where the data protection is high, it is important to ensure the privacy of customers and personnel. This can be achieved by our pseudonomization software Pseudox.
Data analysis is the most efficient way to detect fraudulent behaviors. SimCog identifies typical business processes with complex pattern recognition techniques. Deviations from this norm or conspicious clustering of special cases can be a lead on fraudulent activities. We have experience in identifying indications on misconducting customers or personnel damaging the company. The analysis of internal data has also been shown to have preventive effects.
SimCog’s Pseudox can be used for pseudonomization of emails and other texts to render names, addresses, telephone numbers, etc. unrecognizable. For this purpose, we built an algorithm that combines Natural Language Processing (NLP), meta information, the structure of letters and other methods is used. At the moment, Pseudox can pseudonomize texts in english, german, and spanish. Pseudox was granted the ePrivacy Seal by ePrivacy.
Prediction of Maintenance Intervals
For your company, SimCog develops an automated anticipatory predictive maintenance to avoid machine outage, adapted to the needs of your company. For this purpose, predictions on workload and performance of machines and components are provided. In addition, a prognosis on error messages and expected maintenance times is generated. Making use of complex machine learning methods, SimCog predicts the maintenance necessity of individual components.
Avoiding Machine Outages
By a smart combination of sensor data, the probability of outages will be calculated. Thus, an intervention can be planned, before individual sensors produce a warning. Productivity is raised by avoiding expensive machine outages.
Permanent Monitoring of Sensors
The early detection of error messages allows for a swift reaction on maintenance requirements. The maintenance schedules can be optimized, and a permanent monitoring of machine sensor data is given to enable early tactical action.
SimCog has developed new predictive analytics methods to improve your marketing channels.
In todays’ economy a customer centric behavior is indispensable. Individualized customer approaches and the accompanying deployment of various marketing channels become winning strategies in customer acquisition and customer relationship management. SimCog provides Big Data analysis to determine the effects of your marketing efforts.
Optimizing Marketing Spend Efficiency
Assessing the effectiveness of different marketing channels is a major challenge for companies. Often marketing efforts are increased in times that already exhibit increased sales independent of marketing efforts (e.g. Christmas). Therefore, it is not self-evident to what extent marketing efforts have improved customer acquisition and to what extent seasonality or special events play a role.
By means of comprehensive pattern recognition on sales, the influence of your marketing efforts can be assessed in a clean way. Based on the self-learning SimCog algorithms, non-linear patterns can be recognized. This method enables SimCog to separate each marketing channel and predict the real effect on sales of each measure.
The provided information allows for further optimization of marketing budgets. Moreover, the separate effects of the utilized marketing channels may be assessed, ultimately resulting in an optimized marketing mix.
Reduction of Churn Rates
Effective customer relationship management is accomplished by a proper customer approach as well as appropriate efforts to achieve retention of existing customers. Therefore, the identification of contractual customers or subscribers with a high risk of leaving is necessary as well.
Historical customer data in combination with e.g. geodata regarding buying power enables SimCog to create a churn model. The churn model helps the SimCog customers to identify a large portion of potentially dissatisfied customers with minimum effort.