modefinance at the Reply Xchange 2018

Back to ten years ago self-driving cars were still considered the daydream of a wishful thinker. Today, thanks to fast-growing technologies, we don’t even get surprised anymore when talking about the upcoming launch of flying cars.

With the beginning of the so-called 4th industrial revolution has thus become increasingly important get updated on the major innovation trends that are changing the world and our habits. That’s why we decided to take part in the Reply Xchange 2018, the yearly appointment organized by Reply to discuss about the digital transformation of the contemporary age: a worthwhile networking opportunity to get in touch with some of the most important tech-based businesses’ key players.

During the event we had the opportunity to meet the founder of Google X and Udacity, Sebastian Thrun, as well as to verify the technological progress in developing conversational interfaces capable of replaying the natural language and of simulating the human conversational interaction.Thanks to the application of Machine Learning methodologies we’re facing a general implementation of personal assistant and chatbot capable of taking advantage of the data conveyed by the users to enhance their capabilities of providing relevant answers to customer’s requests.Even in finance, where conversational agents (CA) are already employed to answer customer’s common needs, letting specialists focusing on more challenging tasks.

Providing tools for the automation of standard procedures to improve work engagement is the main purpose of the Artificial Intelligence as well as modefinance’s. On this regard we were invited on Reply’s stage as a successful case study of the application of AI technologies in finance, where we had the opportunity to share MORE, modefinance’s AI-based methodology for Credit Risk assessment.

MORE (Multi Objective Rating Evaluation) is a hybrid model based on Game Theory algorithms’ integration that allows an accurate analysis of all the economic and financial areas of a company (such as solvency, profitability, liquidity, etc.): the better the balance between the different areas, the lower the total financial risk of the examined company or bank.

Unstructured economic and financial data are gathered together according to preset parameters and organized within a rational system (AI); then they are integrated with Game Theory models which evaluate the strategic data interaction to obtain the best fitting solution. Those models follow the Fuzzy Logic, a set theory which assigns a truth value to each variable according to its probability.

The application of MORE methodology allows to avoid black box models (i.e. systems which can be viewed in terms of inputs and outputs without any knowledge of their internal process) and ensure transparent and accurate outcomes.

The evaluation of the quantitative parameters is therefore completely automated, while financial analysts can focus on data interpretation process and on qualitative parameters analysis (i.e. variables which can not be mathematically measured through the application of a statistical model or system) for a complete and reliable rating assessment.