Page 27 - FIGI - Big data, machine learning, consumer protection and privacy
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as opposed to a non-bank). This may also increase   and data subjects to foresee consequences of their
            the broader range of data available about consumers,   actions”) will operate.
            and so enrich and plug gaps in the data ecosystem.
               These potential advantages need to be weighed in   4�2  Protecting consumers from bias and discrimi-
            light of how the alternative credit market is develop-  natory treatment
            ing. Loans made using alternative data and automat-
            ed decisions are often small (e.g., to tide someone   Biased inferences and decision-making outputs
            over until the end of the month), and so their results   While one concern arising with big data is how input
            are possibly of limited utility. The new and grow-  data, such as name, age and other personal data, will
            ing market in automated lending using proprietary   be used and protected, another relates to the infer-
            algorithms to evaluate borrowers with no traditional   ences that result from processing such data. Just as
            credit history is also highly innovative. Requiring new   important as the accuracy of the input data is the
            innovative lenders to share their lending results may   manner and accuracy of the inferences big data and
            deprive them of some of the benefits of their invest-  machine learning will draw from it about individuals
            ment and first mover advantage. In addition, such   and groups, and the impact of such inferences on
            firms are often entrepreneurial start-ups that may   decisions.  Some such inferences, which predict
                                                                       117
            struggle with weighty reporting obligations as they   future behaviour and are difficult to verify, may
            seek to grow a risky business. Some do not even rely   determine how individuals are viewed and evaluated
            on credit reference bureau data themselves for their   and so affect their privacy, reputation and self-de-
            own lending decisions (relying entirely on alterna-  termination.
            tive data), which may weaken the logic of reciproci-  Data protection laws that govern the collec-
            ty inherent in credit reference bureaus (where those   tion, use and sharing of personal data typically do
            supplying data are entitled to rely on the wider pool   not address the outputs of machine learning mod-
            of aggregated data supplied by others). 116        els that process such data. One of the concerns of
               For these reasons, it is important to consider the   data protection and privacy law and regulation is to
            overall data environment of the financial market as   prevent discrimination. Principle 5 of the High Level
            it develops, both in relation to the accuracy of data   Principles for Digital Financial Inclusion states that
            used in automated decisions and how responsibility   data should “not be used in an unfair discriminatory
            for accurate data should be allocated in the formal   manner in relation to digital financial services (e.g., to
            credit data reporting systems and more generally.  discriminate against women in relation to access to
               Given  the  wide  range  of  data  available  and  its   credit or insurance).” 118
            varying  sources  and  levels  of  reliability,  there  are   Recent examples of inferences involving major
            numerous policy dilemmas to come regarding how     internet platforms concern sexual orientation, phys-
            the guidelines on clarity and predictability in the   ical and mental health, pregnancy, race and political
            fourth General Principle Credit Reporting (“The legal   opinions. Such data may be used in decisions about
            and regulatory framework should be sufficiently pre-  whether a person is eligible for credit.  The GDPR
                                                                                                 119
            cise to allow service providers, data providers, users   sets apart special categories of personal data for
                                                               tighter restrictions. While personal data is defined as




                Monetary Authority of Singapore’s FEAT Principles
                Principle 3. Data and models used for AIDA-driven decisions are regularly reviewed and validated for
                accuracy and relevance […]
                Principle 4. [Artificial Intelligence and Data Analytics]-driven decisions are regularly reviewed so that
                models behave as designed and intended.

                Smart Campaign’s draft Digital Credit Standards
                Indicator 2�1�5�0
                Underwriting data and analysis is refreshed at each loan cycle to identify changes in the client’s situ-
                ation.






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