Annotation Services

Data annotation services for the finance industry

Research reveals a transformative trend in the finance industry, with 80% of leaders already implementing or considering robotic process automation (RPA) with machine learning (ML). At the heart of this revolution lies data annotation, the bedrock of effective ML models.

With the aid of an optical character reader (OCR) and data annotation, handwritten letters, cheques, proposals, and financial documents can be read and interpreted with remarkable speed and accuracy. Crucially, data annotation for AI in financial services acts as a safeguard, preventing RPA bots from faltering when the rules change. They unearth patterns in historical data and extract vital information for decision-makers.

This article covers the benefits and outcomes of using data annotation in the finance industry.

Driving efficiency, compliance, and productivity

Full-scale process automation in finance improves the accuracy of financial analysis and forecasting. This requires an intelligent combination of finance robotics with ML and data annotation. Data shows that a robot FTE works 20 hours per day, 7 days a week, and 52 weeks a year. This drives up efficiency and productivity significantly.

According to a prominent management consulting firm, a robot FTE costs only 1/3rd of an offshore employee's salary and 1/5th of an on-site employee's salary. This does not imply that we lay off employees; instead, we can upskill them for strategic tasks that require human analysis. This cost-effectiveness underscores the financial advantages of incorporating data annotation in the finance industry.

AI models powered by ML and data annotation have the potential to drive sustainable growth, enhance operational efficiency, improve customer experience, combat financial fraud, and ensure regulatory compliance in the finance industry.

Outcomes of data annotation in finance

The finance industry, with its vast data volume, necessitates the use of technology to generate precise and actionable outcomes. Data annotation, structuring, and labelling play a pivotal role in this process, offering valuable insights, mitigating risks, and aiding in strategic decision-making. Here are the key benefits of data annotation in finance –

Data accuracy and consistency

Inconsistencies and errors in financial data can lead to flawed decisions. Data annotation and labelling can overcome this challenge by analysing and labelling data points such as trends, transactions, and investment portfolios.

This ensures consistency, unambiguity, and integrity in the data you present to decision-makers and clients.

Better data interpretation and analysis

Interpreting raw financial data can be overwhelming. However, data annotation and labelling can make this process easy by providing context and categorisation. It provides details about the type of transaction, sector classification, risk exposure, and client characteristics.

Business owners can use this information to understand market dynamics, optimise investment portfolios, and identify new opportunities for growth.

Compliance with regulations

Use annotation to identify sensitive data such as personally identifiable information (PII), fraud indicators, and financial transactions accurately. Accurate labelling is necessary for robust data privacy and regulatory requirements and to minimise the risk of non-compliance. This builds trust in the industry and among the customers.

Effective risk management

Data annotation provides granular and structured data to assess and manage risks effectively. Annotators can tag data points with risk indicators, market conditions, and historical trends for a comprehensive view of risk exposure.

Business leaders can quantify risks, craft mitigation strategies, and protect assets and investments. Data annotation and labelling help minimise losses, maximise returns, and navigate market fluctuations.

Accurate financial reports and audits

Prepare accurate financial reports and audits with data annotation and labelling. Accurate labelling and categorisation ensure accuracy and integrity. This helps integrate the data seamlessly with financial systems, performance evaluation, and compliance. It reinforces confidence in investors, shareholders, and business regulators.

Efficient data integration and collaboration

Financial institutions work with a variety of internal and external data sources. Data annotation and labelling facilitate integration and collaboration across diverse sources. One can analyse and share data effectively with different teams, departments, and systems for cross-functional insights and data-driven decision-making.

Faster innovation

Data annotation and labelling in financial data apply to advanced analytical models, algorithms, and tools. They help identify emerging trends and patterns to adjust internal strategies, giving financial institutions a competitive advantage in a fast-paced business landscape.

How can Infosys BPM help?

The data annotation and labelling services at Infosys BPM power self-learning AI/ML models in the financial sector. Preparing the training data includes ingestion, creation of workflow queues, performing annotations, and quality checks.

Read more about data annotation services for the financial industry at Infosys BPM.