As we accelerate toward a digital future, consumers are becoming increasingly empowered to make informed decisions on the back of the ready availability of information and greater awareness. This, in turn, has led to cut-throat competition in a cluttered market. Companies are increasingly investing in AI and machine learning to automate repetitive tasks and improve the overall consumer experience in order to retain customers in such a competitive environment and maintain a competitive edge.

However, building ML models can be a herculean task. ML projects typically require thousands or even millions of labelled training data to identify and classify information like humans successfully do and also make predictions. According to McKinsey Global Institute, 75% of AI and ML projects demand learning datasets to be refreshed once every month, and 24% of AI and ML models require a daily refresh of annotated datasets. Data annotation is the process of labelling unstructured data and information to train machine learning models. Although integral, it is one of the most time-consuming and labour-intensive parts of ML projects. Accurately labelled, high-quality data is used to help supervised machine learning models identify objects, understand the sentiment, and perform functions like speech recognition or even driving. Without accurately labelled data, AI system efficacy will be inadequate and disastrous, especially in the automotive, healthcare, and finance industries.

Companies in various industries, including automotive, finance, agriculture, healthcare, and e-commerce, increasingly emphasise data-centric AI and MLOps to build and scale their ML models. A significant part of this involves leveraging high-quality data annotation techniques. According to a report by Statista released in 2020, the fintech sector has emerged as an early adopter of big data.

With a rising focus on automated and smart solutions, the need for data annotation services has skyrocketed over the last couple of years. It must be noted that data annotation is vast and includes services like data extraction, aggregation, extraction, classification, transcription, sentiment analysis, data standardisation, and verification. These services can help companies in the finance industry to understand their customers in a better way. The massive volume of data exchanged in the financial sector gathered through sources like online transactions, customer documentation and further structured and classified by data annotation is shaping fintech in several ways.

How data annotation is streamlining the Indian fintech sector

• Back Office Support: Companies are automating redundant finance back-office processes like report generation, invoice data extraction, and know your customer (KYC) registration. Additionally, machines are trained to verify documents during the process. This enables financial companies to increase productivity and focus on core areas of the process. To automate repetitive tasks like reporting and reconciliation processes, data annotation providers extract and annotate critical data like customers’ personal details, transaction history, and transaction patterns from documents, including invoices, expenses, credit, shipping, and tax. Personally identifiable information (PII) compliance is one of the top requirements that must be ensured during such projects. Hence it is advisable to check for formal certifications that affirm the provider’s commitment to data security and privacy control. After services like data aggregation, cleaning, standardisation, and verification are completed, the data is sent to strategic teams for analysis of various aspects like market patterns, risks, and transaction patterns.
• Customer Service: Conversational AI is an essential element of the customer service process. With the incorporation of AI models, businesses have improved customer experience by a significant proportion. Natural language processing (NLP) and natural language understanding (NLU) technologies are driving the development of next-generation chatbots and conversational AI products across sectors. Conversation AI requires data annotation support such as named entity recognition and extraction, named entity classification, and sentiment and intent analysis to improve the NLP model. Audio transcription experts help customers improve business operations like quarterly meetings, performance discussions, and future planning by leveraging machine learning technology and RPA. Audio annotation and transcription of earnings calls and company meeting calls containing critical and exclusive financial information allow companies to have real-time information and gain a competitive advantage in the market. The call transcripts from earnings calls are useful for making financial decisions and sales and product launches. Earnings call sentiment analysis can be performed to understand the amount of positive, neutral, or negative sentiments involved in the sentences from the earnings call of the data.  
• Financial Advisory: Financial advisors with AI can not only automate administrative tasks like data entry but also significantly impact the client-advisor relationship. With services like data extraction, natural language processing, and sentiment analysis, AI models have the potential to help wealth managers sustain and drive new growth, create operating efficiencies, and transform the customer experience through more hyper-personalised insights and products.
• Risk Management: Banks and financial technology (fintech) companies are implementing risk management systems with AI solutions to facilitate decision-making processes, reduce credit risks, and provide financial services tailored to their users through automation and machine learning algorithms. AI’s ability to analyse large amounts of information substantially improves identifying data relevant to cybersecurity risk management, risk assessment, and accurate business decision-making. Services include fraud detection, threat analysis, data classification, and anomaly detection in market movements.

Today, several companies are partnering with external data annotation solution providers to advance AI and machine learning in finance. For instance, technology services company CrowdReason, which specialises in innovating and maintaining high-value automation solutions to leverage robotic process automation, machine learning, and blockchain technologies, had previously faced challenges in correcting inaccurate field extractions for its product, TotalPropertyTax. To correct inaccurate field extractions and create a workflow to improve algorithmic performance, the company leveraged iMerit’s data annotation solution, resulting in improved algorithm performance, time savings of 80% for CrowdReason employees, and an improved user experience.

Similarly, Bill.com, a provider of cloud-based software that simplifies and automates the financial process, previously faced accuracy issues with its virtual assistant’s field extraction algorithms. They faced the prospect of correcting the data manually. Using iMerit’s tool-agnostic approach for data annotation, the company created new training datasets. After analysing the models, Bill.com noted that accuracy in field extraction algorithms had improved by 5%. As a result, employees were able to focus on other core areas of the business, which improved customers’ experience considerably.

Data annotation can do wonders for financial companies because of the availability of real-time data generated every millisecond. Advancements in data annotation services and ethical use of such services have undoubtedly helped India emerge as the centre of transformation and innovation in fintech. There is no doubt that high-quality data annotation will streamline India’s fintech sector.

https://blog.ipleaders.in/fintech-industry-delving-interiors/