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Robo Advisors are financial advisory apps that profile users into risk classes before providing financial advice. This risk profiling of users is of functional importance and is legally mandatory. Irregularities at this primary step will lead to incorrect recommenda- tions for the users. Further, lack of transparency and explanations for these automated decisions makes it tougher for users and regulators to understand the rationale behind the advice given by these apps, leading to a trust deficit. Regulators monitor this pro- filing but possess no independent toolkit to “demystify” the black box or adequately explain the decision-making process of the robo financial advisor.
Our paper proposes an approach towards developing a ‘RegTech tool’ that can explain the robo advisors decision making. We use machine learning models to reverse engi- neer the importance of features in the black-box algorithm used by the robo advisor for risk profiling and provide three levels of explanation. First, we find the importance of inputs used in the risk profiling algorithm. Second, we infer relationships between inputs and with the assigned risk classes. Third, we allow regulators to explain decisions for any given user profile, in order to ‘spot check’ a random data point. With these three explanation methods, we provide regulators, who lack the technical knowledge to understand algorithmic decisions, a method to understand it and ensure that the risk-profiling done by robo advisory applications comply with the regulations they are subjected to.
The COVID-19 pandemic is posing unique challenges to policymakers across the globe, necessitating efficient action in short timeframes. During such crises, having the right data at the right time is crucial to making informed policy decisions. Traditional economic indicators can be inadequate owing to issues of timeliness, granularity, and difficulty in collection. There is a need therefore for higher-frequency and more granular data to track economic activities. These “alternative” or “proxy” high-frequency indicators could help assess the economic impact triggered by COVID-19, shape new economic policies, and understand the road to recovery. This brief argues for the creation of a publicly accessible, multi-sectoral dashboard based on sectoral, high-frequency indicators.
As India completes 50 days of lockdown, this report presents the findings of a data-driven enquiry into the extent to which the lockdown has achieved its health objectives and arrested the spread of COVID-19. The success is measured on four parameters: flattening the curve, reducing the growth rate of new cases, containing the spread, and improving healthcare capacity. The findings show that while the lockdown has flattened the curve to an extent, it has failed to reverse the trend or contain the disease. Significant changes in strategy would have to be adopted to arrest the spread of COVID-19.