Banking&Insurance
Correlating information for better direct marketing
Background
To carry out customized marketing campaigns, it is necessary to know how to profile your customers well, through constant updating of company databases that requires correlation, verification, and modification of information.
Solution
The implemented system combines semantic analysis, data structure analysis, and ML and NLP techniques that can understand, and constantly learn from new data, analyze structured (NoSQL; SQL) and unstructured (CSV; PDF; Excels) sources, and return correlated data.
Results
Optimizing the process of updating and increasing database information, profiling customers with personalized offers, and improving direct marketing actions. In addition to this, data enrichment actions, that is, acquisition of new information from external sources.
Data masking to comply with GDPR and not lose information
Background
The activity of sharing sensitive information and data to third parties, on hybrid clouds, through masking techniques–without altering the statistical properties of the data–is handled manually, causing a major slowdown in the operational process and a great deal of economic expense.
Solution
The system was developed on highly complex natural language analysis and Machine Learning techniques and is capable of analyzing any type of information (structured and unstructured data).
Results
GDPR-compliant data , minimization of data masking interference, independent identification of the most suitable type of concealment, reduction of cost, time, and risk of information loss, and learning and analysis of new data.
Automatic recognition and sorting of requests
Background
For those who work with large numbers of clients, handling requests and communications, in general, must be streamlined and precise. Improving customer service processes means automating the recognition of communications and handling of inquiries, coming in from multiple entry points (agencies of the same company and/or direct customers).
Solution
The artificial intelligence technology used is based on ML and OCR systems: this enables theemail recognition(entity recognition) and theanalysis of the content and its attachments, the verification of the correctness of the extracted entities and, finally, thecorrespondence between these and the entities in the database.
Results
The solution ensures automatic and timely congruence detection, active communication with customers, the same company’s network of agencies and offices, and automatic compilation and updating of management systems.
