AI-Driven risk assessment: Revolutionizing audit planning and execution

Finance & Accounting Research Journal, Journal Year: 2024, Volume and Issue: 6(6), P. 1069 - 1090

Published: June 15, 2024

Artificial Intelligence (AI) is profoundly transforming risk assessment in audit planning and execution, offering unparalleled advancements efficiency, accuracy, strategic decision-making. This review explores the role of AI-driven revolutionizing process, highlighting its benefits challenges associated with implementation. AI technologies, particularly machine learning advanced data analytics, are enhancing auditors' ability to identify, assess, manage risks. Traditional methods often rely on historical static models, which can be limited their predictive power. In contrast, approaches leverage vast datasets, continuously updating from new information provide dynamic precise evaluations. One primary process analyze large volumes rapidly. algorithms identify patterns anomalies that may indicate potential risks, might missed by human auditors due cognitive biases or overload. capability ensures a more comprehensive accurate assessment, enabling focus high-risk areas allocate resources effectively. Moreover, enhances audits. By providing real-time insights into emerging allows anticipate address issues proactively. forward-looking approach not only improves efficiency execution but also strengthens overall management framework organizations. Despite these advantages, integrating poses several challenges. Ensuring quality integrity crucial, as systems relevant produce reliable assessments. Additionally, "black box" nature some models create transparency issues, making it difficult for explain how specific assessments were derived. Addressing algorithmic ensuring compliance regulatory standards critical concerns. conclusion, detect risks greater precision efficiency. However, fully realize potential, must navigate related quality, transparency, ethical considerations. doing so, profession technologies achieve robust effective practices, ultimately organizational resilience accountability. Keywords: AI-Driven, Risk Assessment, Revolutionizing, Audit Planning Execution

Language: Английский

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AI-Driven Chatbots in CRM: Economic and Managerial Implications across Industries

Administrative Sciences, Journal Year: 2024, Volume and Issue: 14(8), P. 182 - 182

Published: Aug. 19, 2024

In the era of digitization and technical breakthroughs, artificial intelligence (AI) has progressively found its way into field customer relationship management (CRM), bringing benefits as well difficulties to businesses. AI, particularly in context CRM, employs machine learning (ML) deep (DL) techniques extract knowledge from data, recognize trends, make decisions, learn mistakes with minimal human intervention. Successful firms have effectively integrated AI CRM for predictive analytics, computer vision, sentiment analysis, personalized recommendations, chatbots virtual assistants, voice speech recognition. AI-driven chatbots, one AI-powered systems, arose a disruptive approach service, such, unfolded economic managerial ramifications CRM. Given literature’s focus on other there is an obvious need investigation industry applications implications The purpose this study explore elucidate within systems. This aims provide comprehensive understanding how these technologies can enhance interactions, streamline business processes, impact organizational strategies. To reach goal, conducts comparative qualitative analysis based many interviews experts contributors field. Interviews specialists yielded insights use their industry. primary advantages identified were cost, efficiency, performance. addition, proven useful variety industries, including retail tourism. Nonetheless, limitations usage healthcare system, terms ethical problems.

Language: Английский

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Artificial Intelligence in the Indian Banking System: A Systematic Literature Review

SSRN Electronic Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

The emergence of AI and its transformational potential in the banking domain has been a focus great interest recent years, particularly with respect to financial landscape developing nation like India. objective this paper is study evolution track depth impacts Indian Banking – adoption, significance, challenges case studies implementation. This section introduces definition how it contributed restructuring modern businesses, context sector services. With digital technologies marching on, new era already begun take off baking sector-which replaced age-old practices automated, tech-led practices. Artificial Intelligence (AI) as hands-on technology contributes various sectors regulatory compliance, systematized fraud detection, prudent risk management, improved user experiences. cites that enumerate benefits related implementation detail. are mile wide, include everything from sophisticated evaluations preventative actions enhanced service client. Data theft privacy concerns, job displacement some stimulate essential debates about usefulness AI. also attempts comprehensive discussion influencing across different facets process- approach customer relationship. Transformation: transformed Risk Management (risk management detection measures), CAUTION (efficiency, automation accuracy) UX (personalisation or customisation products services). Moreover, successful demonstrated which provide critical explanation transformative impact operations e.g.: personalized State Bank India- powered chatbots Better processes experience strong argument supporting need for banking. Wrapping up, enough covered show scope barriers deploying Sector It emphasises address matters such data privacy, security challenges, legal compliance generative

Language: Английский

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Artificial Intelligence for Financial Accountability and Governance in the Public Sector: Strategic Opportunities and Challenges

Administrative Sciences, Journal Year: 2025, Volume and Issue: 15(2), P. 58 - 58

Published: Feb. 11, 2025

This study investigates the transformative capacity of artificial intelligence (AI) in improving financial accountability and governance public sector. The aims to explore strategic potential constraints AI integration, especially as fiscal systems become more complex expectations for transparency increase. employs a qualitative case methodology analyze three countries, which are Estonia, Singapore, Finland. These countries renowned their innovative use administration. data collection tools included an extensive review literature, governmental publications, studies, feedback. reveals that AI-driven solutions such predictive analytics, fraud detection systems, automated reporting significantly improve operational efficiency, transparency, decision making. However, challenges algorithmic bias, privacy issues, need strong ethical guidelines still exist, these could hinder equitable AI. emphasizes importance aligning technological progress with democratic values by addressing problems. also enhances dialog around AI’s role It provides practical recommendations policymakers who seek wisely promote trust, ensure governance. Future research should focus on enhancing frameworks investigating scalable overcome social technical integration.

Language: Английский

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AI and ethical accounting: Navigating challenges and opportunities

International Journal of Advanced Economics, Journal Year: 2024, Volume and Issue: 6(6), P. 224 - 241

Published: June 15, 2024

Artificial Intelligence (AI) is revolutionizing the accounting profession, offering transformative capabilities for automating tasks, enhancing decision-making, and improving financial accuracy. As AI becomes integral to practices, it brings both significant opportunities notable ethical challenges. This review examines intersection of accounting, providing insights into how professionals can navigate evolving landscape. The adoption in introduces increased efficiency systems handle repetitive tasks such as data entry, reconciliation, transaction categorization, freeing accountants focus on strategic activities. Advanced algorithms analyze large volumes identify patterns, detect anomalies, provide real-time insights, decision-making forecasting Moreover, AI-driven predictive analytics aid risk assessment management, helping organizations anticipate mitigate potential threats. However, integration also raises concerns. One primary challenges ensuring transparency accountability processes. often operate "black boxes," understanding explaining their outputs be difficult, potentially leading issues trust compliance. Ethical necessitates that designed with mind, clear explanations decisions actions. Data privacy security represent another critical consideration. extensive use by robust measures protect sensitive information from breaches unauthorized access. Accountants must ensure comply protection regulations standards, safeguarding confidentiality integrity data. Bias fairness are pressing issues. If not properly addressed, biases lead unfair outcomes, biased recommendations or discriminatory practices. Ensuring requires ongoing monitoring evaluation biases. In conclusion, while offers substantial benefits presents carefully managed. these promoting transparency, security, addressing systems. By doing so, profession harness upholding standards maintaining public trust. Keywords: AI, Accounting, Navigating, Challenges, Opportunities.

Language: Английский

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The Intersection of Sustainability, Responsible Analytics, and Ethical AI

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 313 - 332

Published: April 18, 2025

In an era of rapid technological progress and global challenges, integrating AI with sustainable practices, responsible analytics, ethical principles is essential. This chapter presents integrated framework for innovation by synthesizing insights from literature (2018–2024) based solely on secondary data reputable academic sources industry reports. It examines how AI-driven solutions can align sustainability objectives while ensuring fairness, transparency, accountability. The discussion highlights intersections among environmental stewardship, data-driven decision-making, design, offering actionable recommendations policymakers leaders. demonstrates benefits such as improved energy efficiency, economic performance, social equity, addressing challenges like quality evolving standards. Ultimately, it provides objective, evidence-based guide future research practice in development.

Language: Английский

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Enhancing audit accuracy: The role of AI in detecting financial anomalies and fraud

Finance & Accounting Research Journal, Journal Year: 2024, Volume and Issue: 6(6), P. 1049 - 1068

Published: June 15, 2024

Artificial Intelligence (AI) is transforming the field of auditing by significantly enhancing ability to detect financial anomalies and fraud. The integration AI in processes offers unprecedented capabilities for analyzing vast datasets with greater speed precision than traditional methods. This review explores impact on audit accuracy, focusing its role identifying irregularities fraudulent activities. AI-driven tools leverage machine learning algorithms advanced data analytics scrutinize records a high level detail. These can process extensive amounts rapidly, patterns deviations that may indicate or behavior. Unlike conventional techniques, which often rely sampling manual checks, evaluate entire datasets, ensuring comprehensive coverage reducing likelihood undetected issues. One primary benefits enhance anomaly detection. Machine models are trained recognize normal behaviors flag warrant further investigation. capability particularly valuable subtle complex fraud might be missed human auditors. For example, unusual transaction patterns, inconsistencies statements, vendor customer behaviors, common indicators Moreover, AI's predictive proactively identify potential risks historical forecasting future trends. allows auditors anticipate areas concern allocate resources more effectively, improving overall efficiency effectiveness process. Additionally, systems continuously learn adapt, their accuracy reliability over time. Despite advantages, implementation also presents challenges. Ensuring quality integrity, addressing algorithmic biases, maintaining transparency decision-making critical considerations. Auditors must stay updated evolving technologies regulatory requirements maximize while mitigating risks. In conclusion, holds significant promise detection By integrating into practices, organizations achieve thorough reliable audits, ultimately strengthening oversight integrity. However, careful management associated challenges essential fully realize domain. Keywords: Fraud, Financial Anomalies, AI, Audit Accuracy, Detecting.

Language: Английский

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Ethical AI in Financial Inclusion: The Role of Algorithmic Fairness on User Satisfaction and Recommendation

Published: July 22, 2024

This study explores the impact of artificial intelligence (AI) on financial inclusion satisfaction and recommendation, focusing ethical dimensions perceived algorithmic fairness. From perspectives organizational justice theory heuristic-systematic model, we examine how constructs algorithm transparency, accountability, legitimacy influence users' perceptions fairness, subsequently, their with recommendation AI-driven inclusion. Through a survey-based quantitative analysis, our results indicate that fairness acts as mediating factor between attributes AI systems user well recommendation. Findings reveal higher levels enhance customers' which in turn significantly increases both services facilitated by likelihood to recommend them. research not only contributes literature ethics highlighting critical role transparent, accountable, legitimate practices fostering among users, but also fills significant gap understanding implications contexts.

Language: Английский

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AI-Powered Financial Inclusion

Advances in finance, accounting, and economics book series, Journal Year: 2024, Volume and Issue: unknown, P. 449 - 480

Published: Dec. 13, 2024

Financial inclusion is crucial for both economic growth and decreasing poverty levels, however, around 1.7 billion adults worldwide still do not have access to banking services. AI-driven financial technologies offer creative solutions close this divide through offering easy, cost-effective, effective This chapter delves into the present situation of inclusion, main AI driving changes, successful examples, obstacles, future outlook. Through use artificial intelligence, we ability develop a system that more inclusive, giving power individuals promoting growth.

Language: Английский

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Integrating machine learning algorithms into audit processes: Benefits and challenges

Finance & Accounting Research Journal, Journal Year: 2024, Volume and Issue: 6(6), P. 1000 - 1016

Published: June 15, 2024

The integration of machine learning (ML) algorithms into audit processes represents a significant advancement in the field auditing, offering substantial benefits terms efficiency, accuracy, and risk management. This review examines transformative potential ML highlighting its key challenges that must be addressed to fully leverage capabilities. Machine algorithms, with their ability analyze large datasets identify patterns, enhance accuracy thoroughness audits. Traditional auditing methods often rely on sampling manual checks, which can miss anomalies fraudulent activities. In contrast, process entire datasets, uncovering subtle patterns irregularities may indicate fraud or errors. comprehensive analysis reduces oversight improves reliability findings. One primary is capacity for anomaly detection. models trained historical data understand normal financial behavior flag deviations might signify irregularities. detect real-time enables auditors issues promptly, reducing time lag between occurrence detection fraud. Predictive analytics, powered by ML, further enhances forecasting future risks based data. proactive approach allows anticipate mitigate before they materialize, contributing more robust management strategies. Despite these advantages, integrating presents several challenges. Ensuring quality integrity crucial, as are only good analyze. Poor-quality lead inaccurate predictions conclusions. Additionally, "black box" nature some pose transparency issues, making it difficult explain how specific conclusions were reached, critical stakeholder trust regulatory compliance. Another challenge algorithmic bias. inadvertently perpetuate existing biases data, leading unfair skewed outcomes. Continuous monitoring validation necessary such biases. conclusion, while offers management, also necessitates careful attention quality, transparency, bias mitigation. Addressing essential realize enhancing practices. Keywords: Benefits, Challenges, Audit Processes, Algorithms, ML.

Language: Английский

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