Prometeia supports banks and insurers in the path to digital transformation, defined by the rise of fintech and new digital native players, through the enhancement of internal and external data, with the adoption of Big Data and Data Science methodologies and tools to integrate traditional analytical techniques.
Within an environment of rapid technological and regulatory evolution, Prometeia supports banks in becoming ‘data driven’. Being a data driven bank means managing and leveraging your customer and business information assets by applying algorithms to gain competitive advantage, optimize decision-making and operating processes, develop new products and innovate business models.
The insurance industry is experiencing the advent of new challenges and business models, requiring a more advanced and structured approach to data. Prometeia offers its experience to drive the data strategy of Insurance companies and support them to deploy it through direct actions to the core processes, from Customer Value Management to Fraud Analytics to Claims Management.
Text Analytics is the process of extracting actionable knowledge from unstructured text data. Prometeia employs statistical techniques such as topic mining and sentiment analysis to reveal information hidden in large amount of data and allow business applications in Customer Management, Credit Risk Management, process automation and similar areas.
We live in an interconnected world, where individuals’ behaviour is the result of their interactions with the society. Prometeia employs the innovative method of Link Analytics to mine the knowledge hidden in social networks, enabling new business opportunities in credit risk, marketing and anti-fraud.
Artificial Intelligence (AI) is the machines’ ability to mimic the cognitive behaviour of humans. Prometeia is leveraging on AI to provide smart solutions able to transform business operations in the financial and insurance space.
Machine Learning is the branch of AI dealing with the development of algorithms capable of 'learning'. The algorithm, in the 'supervised' form, takes in input a set of data that will be used to recognize patterns. When confronted with new data, it should be able to produce consistent classification functions. In the 'non supervised' mode, the algorithm tries to discover relevant relations of 'closeness' and produces its own interpretation of similar patterns. Prometeia uses and implements a variety of Machine Learning techniques with applications ranging from risk analysis, Customer Relationship Management, to the development of economic and financial forecasting models.
Customer Value and Customer Risk both stand at the centre of our clients’ business, however they are typically dealt with independently. Thanks to its industry-specific and advanced analytics competencies, Prometeia adopts an integrated view of both value and risk, supporting the accomplishment of strategic goals through dedicated commercial campaigns.
Prometeia employs a modular approach to Fraud Analytics tailored on the client’s maturity level. Leveraging on existing processes and data sources, a roadmap to data driven improvement is identified through processes upgrade, advanced analytics techniques (Text Analytics, Machine Learning, Link Analytics) and extension to external data sources.
Claims management is traditionally a very time consuming and resource intensive activity for insurance companies. Prometeia employs techniques such as Text Analytics, Machine Learning and AI to drastically reduce costs and improve both efficiency of claims department and final user experience.
Payments landscape is changing rapidly towards improved customer experience. Prometeia, through the usage of link analytics, text analytics and machine learning methodologies, can extract value from transaction data to provide valuable insights for the business and build a flawless payment experience for the client.
Prometeia produces 'fair value' estimates of financial products, consistent with the credit worthiness of the issuer. The market often does not provide a direct reading of the risk level, which must be inferred differently. In Prometeia we train neural network machines to 'learn' ratings and 'credit spread' (CDS) from balance sheets and market data, using financial institutions with readily available ratings and CDS as training sets. The trained network is later used to produce market consistent CDS and ratings for non rated companies (shadow rating approach).