Category V ariable Definition Tax Avoidance CETR Cash Effective T ax Rate = Cash Taxes Paid / Pre - tax Income GETR GAAP Effective Tax Rate = T otal Tax Expense / Pre - tax Income CETR3 Three - year average CETR GETR3 Three - year average GETR CETR5 Five - year average CETR GETR5 Five - year average GETR A_CETR Adjusted Cash Effective Tax Rate A_GETR Adjusted GAAP Effective T ax Rate A_CETR3 Adjusted three - year average CETR A_GETR3 Adjusted three - year average GETR A_CETR5 Adjusted five - year average CETR A_GETR5 Adjusted five - year average GETR TSTA Total Book - T ax Difference (accrual - based measure) TSDA Discretionary Book - Tax Difference (discretionary accrual - based measure) Profitability ROA Return on Assets = Net Income / Lagged T otal Assets ROE Return on Equity = Net Income / Lagged Equity CFO Operating Cash Flow scaled by total assets LOSS Loss dummy (1 if prior - year net income < 0) Stability LEV Leverage = T otal Liabilities / Total Assets CUR Current Ratio = Current Assets / Current Liabilities SIZE Natural logarithm of total assets PPE Ratio of Property, Plant, and Equipment to total assets AGE Natural logarithm of firm age (based on year of establishment) INVREC Ratio of inventories and receivables to total assets Growth GRW Sales growth rate MB Market - to - Book Ratio = Market Capitalization / Book Equity TQ Tobin's Q = (Market Capitalization + Total Liabilities) / T otal Assets Market Valuation & Governance KOSPI KOSPI listing status dummy BIG4 Big4 audit dummy FORN Foreign ownership share (%) OWN Largest shareholder ownership share (%) Stability Measures Stability measures reflect a firm's financial soundness and its ability to meet obligations. Leverage (LEV) is defined as total liabilities divided by total assets, indicating the firm's degree of financial leverage. The current ratio (CUR), calculated as current assets divided by current liabilities, captures short - term liquidity and payment capacity. Firm size (SIZE) is measured as the natural logarithm of total assets, providing a quantitative indicator of scale. The proportion of property, plant, and eq uipment (PPE), defined as tangible fixed assets divided by total assets, is used to assess the structural stability of the asset base.

The head of the UK's financial regulator is to warn that banks, investors and insurers will have to ramp up their spending to combat scammers using artificial intelligence to commit fraud. Nikhil Rathi, the chief executive of the Financial Conduct Authority (FCA), will say that there are risks of "cyber fraud, cyber-attacks and identity fraud increasing in scale and sophistication and effectiveness" as artificial intelligence (AI) becomes more widespread, in a speech in London on Wednesday. Rapid advances in the sophistication of generative AI by companies such as OpenAI and Midjourney have set companies scrambling to work out how to use the technology to improve productivity. The technology has also prompted concerns over the ease with which users can fake language, audio and video. The prime minister, Rishi Sunak, is hoping to make the UK a centre for the regulation of AI.

Artificial intelligence (AI) is often touted as the cure-all for financial services firms' ability to deal with the looming data onslaught stemming from environmental, social & governance (ESG) regulation. Yet ESG also poses an existential threat to the financial services industry's use of AI The European Union's Sustainable Finance Disclosure Regulation has required asset management firms to begin collecting millions of data points from the companies in which they invest, and the forthcoming Corporate Sustainable Reporting Directive will only add to the volume of data points. Further, there is the data being collected under the Task Force on Climate-Related Financial Disclosures (TCFD) initiative and the International Sustainability Standards Board's plans to create a baseline for ESG reporting. Taken all together and it becomes clear that AI-enabled systems will be essential to firms' efforts to make sense of -- and profit from -- all these requirements. The carbon footprint from storing and processing data is enormous and growing, algorithms have already been shown to discriminate against certain groups in the population, and a lack of technology skills in both senior management ranks and the general workforce leave firms vulnerable to mistakes.

Onfido's 2022 Identify Fraud Report has identified a concerning 47 per cent increase in identity fraud since 2019, with financial services remaining one of the highest targeted sectors. Further research from McAfee reveals cybercrime costs the global economy $600billion annually, while consulting firm Accenture forecasts cyberattacks could cost companies $5.2trillion worldwide by 2024. Global payment card fraud losses, specifically, amounted to $28.58billion in 2020, says a Nilson report. Payments card fraud is such a concern that the UK recently implemented tighter anti-fraud checks on card payments with new Strong Customer Authentication (SCA) rules coming into force in March 2022, activated for almost all online purchases above £25 to provide a greater level of security against fraudsters. With cybercriminals getting ever more inventive with their malicious get-rich-quick schemes, fraud prevention and detection has never been more critical.

Last year, in an article that talked about the impact big data has on finance, we said that location data sets can make investing easier. Companies spent nearly $11 billion on financial analytics in 2020. A large portion of this market is driven by investment companies and mutual funds. This is because accurate data about consumer movement can help you know about consumer trends and corresponding market movements. Today, we are going to look at the potential influence big data has on personal finance in detail.

The U.S. Securities and Exchange Commission (SEC) will seek input on whether digital customer engagement innovations used by financial firms should be governed by existing rules or may need new ones, commission chair Gary Gensler told Reuters. While the SEC's thinking on the subject is at an "early stage," its rules may need updating to account for an artificial intelligence-led revolution in predictive analytics, differential marketing and behavioral prompts designed to optimize customer engagement, he said. The SEC plans to launch a sweeping consultation in coming days that could have major ramifications for retail brokers, wealth managers and robo-advisers, which increasingly use such tools to drive customers to higher-revenue products. I really believe data analytics and AI can bring a lot of positives, but it means we should look back and think about what does this mean for user interface, user engagement, fairness and bias," said Gensler. "What does it mean about rules written ...

Among the recommendations are the introduction of suitability requirements for AI-driven financial services, and add-on capital buffers based on AI algorithms. The OECD has published a new report offering policy recommendations to ensure the use of artificial intelligence (AI), machine learning (ML) and big data in finance is consistent with financial stability, consumer protection, and market integrity and competition objectives. While noting that AI can drive competitive advantages for financial firms, improve their efficiency, and enhance services for consumers, the report says AI applications in finance may create or intensify financial and non-financial risks, and give rise to potential financial consumer and investor protection concerns around the fairness of consumer results, data management and data usage. The report says emerging risks from the deployment of AI techniques need to be identified and mitigated to support and promote the use of responsible AI, and existing regulatory and supervisory requirements may need to be clarified and adjusted to address incompatibilities of existing arrangements with AI applications. In particular, policymakers should consider sharpening their focus on better data governance by financial sector firms to reinforce consumer protection across AI applications in finance, and address risks related to data privacy, confidentiality, concentration of data, and unintended bias and discrimination.

In one 2020 complaint to the Consumer Financial Protection Bureau, a consumer echoed the words of George Floyd to describe an experience with a financial company, saying "you all will not let me breathe." The consumer wanted to know why the firm would "not take their knee off ... my neck?" Another criticized a company for its approach to sexual identity issues. "The employees refused to be sensitive to my pronouns' and name change," the consumer said. "As a result, my account was closed after years of torture from this credit card company."

The AI Software as a Service (SaaS) provider has become a member of the Intel AI Builders program, per a press release seen by Insider Intelligence. The program brings together industry AI developers to access tech resources, comarketing opportunities, and Intel Capital investment to help boost AI development. For context, Abaka offers financial firms of all sizes AI solutions that focus on driving digital customer engagement and providing advice for wealth, savings, and retirement. The Intel partnership strengthens Abaka's goal of becoming a cost-effective platform for financial firms' AI needs amid limited IT budgets. Intel is one of the world's most recognizable tech brands and acts as a stamp of approval on Abaka's AI offering, promoting confidence among financial firms.

Many banks and other financial firms adapted fairly well when COVID-19 struck. They handled the rise in online and mobile transactions, kept in touch with customers and partners while working from home, and adopted social distancing measures as branches and offices reopened. That was the easy part. Now the industry should prepare for a wave of customer inquiries as the effects of the pandemic ripple through the economy. That's likely to put their customer-service centers under stress.