Fairness in machine learning is vital, especially as AI shapes decisions across sectors. In insurance pricing, fairness involves unique challenges due to regulatory demands for transparency and restrictions on using sensitive attributes like gender or race. Traditional fairness methods may not align with these specific requirements. To address this, the authors propose a tailored approach for building fair insurance models using only privatized sensitive data. Their method ensures statistical guarantees, operates without direct access to sensitive attributes, and adapts to varying transparency needs, balancing regulatory compliance with fairness in pricing.
Insurance decisions range from trivial to significant, accumulating impact over time. Intuition can mislead, especially when premiums rise due to risk. Key factors include hazard size, wealth, risk aversion, and insurer margins. Greater transparency in insurance margins can help families make informed choices, improving financial well-being and societal welfare.
This article also has links to a calculator and spreadsheet which apply the framework described herein.
#regulators recently issued #cybersecurity #disclosure guidelines to enhance #transparency and #accountability among firms. A study analyzed cybersecurity disclosure practices among a sample of Toronto Stock Exchange firms over seven years. Findings indicate a notable increase in disclosure after 2017 guidance by #canadian Securities Administrators. However, improvements are needed, especially in #governance and #riskmitigation disclosure. This study sheds light on policy's impact on cybersecurity transparency.
The essential role of #ai in #banking holds promise for efficiency, but faces challenges like the opaque "black box" issue, hindering #fairness and #transparency in #decisionmaking #algorithms. Substituting AI with Explainable AI (#xai) can mitigate this problem, ensuring #accountability and #ethical standards. Research on XAI in finance is extensive but often limited to specific cases like #frauddetection and credit #riskassessment.
Amid #digitalfinance's rise, its role in combating corporate #financialfraud gains attention. The study explores how digital finance curbs fraud via #transparency, #regulation, #riskcontrol, and trust mechanisms. Findings suggest positive impacts on deterring corporate #fraud, with implications for digital finance development and #fraudprevention
The current global #dataprivacy situation resembles the accountability crisis during the early 2000s US accounting scandals. Lack of oversight, #transparency, and #regulation has led to confusion and distrust. By emulating successful models like the Sarbanes-Oxley Act, companies can regain consumer trust by treating privacy policies like #financialstatements, standardized and audited. The proposal includes #privacy #controls similar to financial internal controls and a Privacy Cube framework for #riskmanagement, ultimately aiming to rebuild #consumertrust in #data handling.
The paper highlights the importance of #thirdparty #transparency in the #riskmitigation of #supplychain #climaterisks.