In the conventional method of audit services, a multi-disciplinary team of accountants, forensic professionals, and bankruptcy evaluators analyzes data from diverse sources and performs an audit. This people-intensive methodology requires a significant investment of time and effort to manage audit and assurance activities on an industrial scale.

Infosys partners with professional services organizations to undertake audit and assurance projects by leveraging machine learning algorithms and tools. Machine learning is a catalyst for workforce optimization in our audit engagements. Our solution identifies and integrates diverse data tenors using open source and proprietary machine learning frameworks to uncover knowledge from diverse sources across the enterprise.

Our machine learning solutions learn from common elements across value streams of audit and assurance. Past decisions guide the machine to make informed decisions about audit and assurance workforce optimization strategies. Our solution mines past effort, metrics and knowledge to undertake audit activities such as proposals for similar engagements.

We combine deep data analytics with machine learning to optimize the workforce for audit services.

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Challenges & Solutions

Our machine learning approach uncovers patterns and distills learning from past engagements for prompt and efficient audit services.

We blend digitization with automation to undertake audit services at scale and velocity.

We use support vector machines, ensemble methods, and clustering algorithms to identify workforce optimization opportunities in audit services.