Collects loan requests, assesses creditworthiness and defines a risk class (rate). He crosses these requests with the lenders (typically a bank) according to their credit policies and proceeds to conclude the contract. It also deals with credit monitoring.
k class (rate). He crosses these requests with the lenders (typically a bank) according to their credit policies and proceeds to conclude the contract. It also deals with credit monitoring. Credit assessment is stratified: a part of classic banking logics combined with a deconstructed approach (web reputation and semantic analysis of behavior).

For the purposes of the credit risk assessment process, have integrated the Cerved API which allows us to assess the creditworthiness of VAT numbers. Specifically, already today our evaluation module extracts in real time:
1. all chamber data, company companies and the main dimensional data (number of employees, total assets, turnover, capital);
2. real estate data: detailed information is gi...
Collects loan requests, assesses creditworthiness and defines a risk class (rate). He crosses these requests with the lenders (typically a bank) according to their credit policies and proceeds to conclude the contract. It also deals with credit monitoring.
k class (rate). He crosses these requests with the lenders (typically a bank) according to their credit policies and proceeds to conclude the contract. It also deals with credit monitoring. Credit assessment is stratified: a part of classic banking logics combined with a deconstructed approach (web reputation and semantic analysis of behavior).

For the purposes of the credit risk assessment process, have integrated the Cerved API which allows us to assess the creditworthiness of VAT numbers. Specifically, already today our evaluation module extracts in real time:
1. all chamber data, company companies and the main dimensional data (number of employees, total assets, turnover, capital);
2. real estate data: detailed information is given on the real estate properties divided into buildings, buildings and land. Furthermore, starting from the market value estimates, we determine an estimate of the presumed recovery value: this could be useful for determining a credit exposure ceiling or for assessing the expected loss of the transaction;
3. details of possible negative events: protests, prejudicial, bankruptcy procedures and crisis events. Here, a Down Notching is calculated with respect to the overall evaluation;
4. information on business partners: the composition of all the company's shareholders is shown with details of the investments. NEGATIVE EVENTS and REAL ESTATE are inspected by each member: this information could be useful if there is a policy on collateral to be adopted.
5. Provider Scoring.

We will soon approach the Open Banking platforms (PSDII) we are thinking of a destructible evaluation approach that goes up to the analysis of social profiles (we are studying Facebook's Graph API).
For the investor partners, a reporting system will be available to analyze each individual portfolio of their customers and / or the investments made. The data monitoring tools will support the partners in their consultancy and reporting activities. In addition, the investment destination module was designed in the logic of financial asset allocation, making it possible to establish the expected risk-return trade-off. It is also possible to establish a system for screening loan applications and implement policy
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Employees

Rosario Galletti
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Rosario Galletti CEO Software developer and functional analyst since 2002. Asset Liability Management (ALM) consultant, Banking-Financial Risk Manager since 2012