Appendix 2
Automated decisions including profiling
We carry out various types of profiling, the characteristics of which are described below. Some or all of this profiling may be carried out in a fully automated manner, in accordance with Article 2.7.
1. Modelling and implementation of scoring rules for marketing purposes
The modelling of scoring rules for marketing purposes enables FLOA to find out its customers' and prospects' appetite for a product or service and their preferences, in particular as regards the communication channel used. FLOA can thus adapt its offer (product or service proposed and characteristics of the offer) and the frequency of contact.
The data taken into account for the determination of score models may be all the data concerning You, whether collected directly or indirectly.
We select certain fields that are useful for modelling scoring rules for marketing purposes, which can be correlated with one or more others and then associated with a weighting.
Score rules modelled in this way can be used to :
- establish segments and categories of customers and prospects (e.g. based on behaviour observed when applying for a loan) ;
- identify the appetite of customers and prospects for a product or service;
- measure the reactivity of customers and prospects when they receive and open an offer sent by email / SMS and identify the preferences of customers and prospects with regard to the communication channel used.
FLOA may thus adapt its offers (products or services proposed and characteristics of the offers), the rhythm and the channel of communication in order to respect the choices of its customers and prospects, provide them with quality information and services, adapted to their needs, and improve their satisfaction.
This purpose may have the effect of excluding certain persons from marketing campaigns and/or certain communication channels.
2. Modelling and implementing scoring rules for granting and collection purposes
The modelling of scoring rules for granting and collection purposes enables FLOA to control credit risk (in the case of prospects) and non-payment risk (in the case of customers).
The data taken into account for the determination of score models may be all data concerning you, whether collected directly or indirectly.
We select certain fields that are useful for modelling scoring rules for granting and collection purposes, which can then be correlated with one or more others and associated with a weighting.
In order to better assess the credit risk score, We communicate certain data (personal data, type of contract, amount of credit, repayment methods) to credit information systems ("SIC"), which are governed by the relevant SIC Code of Conduct approved by the Italian Data Protection Authority with Provision no. 163 of 19 September 2019.
These systems are large databases set up to assess credit risk, managed by private individuals and available to many parties.
This means that other banks or financial institutions from which the interested party will ask for another loan, financing, credit card, etc., even to buy a consumer good in installments, will be able to know if he has submitted a recent loan request to us, if he has other loans or financing in progress and if he regularly pays the installments.
For the purposes of concluding the contract and for the purposes of credit protection, creditworthiness assessment as well as for the prevention of over-indebtedness, the loan request submitted by you to us will also be subject to an automated decision-making process based exclusively on the data you provide, on the so-called credit scoring and on any information present in the SICs.
This process may result in the automatic acceptance or rejection of the funding request submitted. In any case, you will always have the right to obtain human intervention in order to be able to express your opinion or contest the decision. The SIC to which We adhere is managed by:
CRIF S.p.A.: with registered office in Bologna, Public Relations Office: Via Zanardi, no. 41, 40131 Bologna, Fax: 0516458940, Tel: 0516458900, website www.crif.it, positive and negative SIC, which includes, as categories of participants: banks, financial intermediaries, private individuals who, in the exercise of a commercial or professional activity, grant deferrals of payment of the consideration for the supply of goods or services.
Score rules modelled in this way can be used to calculate the credit risk (in the case of prospects) and non-payment risk (in the case of customers), enabling the person concerned to :
- subscribe to a product suited to their borrowing capacity ;
- prevent the risk of debt collection/over-indebtedness;
- be protected, in the case of customers identified as "fragile".
This purpose may have the effect of excluding certain people from taking out a loan (refusal to grant), leading to a change in the maximum loan amount or generating the proposal of a product more suited to the borrowing capacity of the person concerned.
3. Modelling and implementing scoring rules to combat fraud
Modelling score rules to combat fraud enables FLOA to identify signals that may help detect fraud, such as the technical and behavioural environments conducive to the action of a potential fraudster.
The data taken into account for the determination of score models can be any data concerning you, whether collected directly or indirectly.
We select certain fields that are useful for modelling score rules to combat fraud, which can then be correlated with one or more others and associated with a weighting.
The implementation of score rules modelled in this way enables FLOA to :
- prevent fraudulent behaviour
- detect fraudulent behaviour
- combat fraudulent behaviour.
This may result in the exclusion of certain individuals from taking out a loan (refusal to grant), the termination of current contractual relations or the initiation of amicable or legal proceedings.