The Banking, Financial Services & Insurance (BFSI) sector has for long been an early adopter of next-generation technology. Not only has it been an early adopter, but CIOs and technology leaders in BFSI are constantly taking advantage of the power of emerging tech to reduce operational bottlenecks and increase efficiency.
The next wave – as you may have rightly guessed – will revolve around using AI/ML and cutting-edge analytics to automate processes and make data-driven decisions. And text analytics has become a key part of this process.
Consider these recent examples to showcase the power of text analytics in the BFSI sector:
- A leading bank in Asia was building an ML model to analyze one particular metric: no. of products sold per customer. The bank was building a big data engine to capture customer demographic data, the current set of financial products sold to them, customer profile based on credit card statements, location and even KYC information. But there was a critical process that would increase the quality of the ML model manifold. That layer was a text analytics engine. Giving the bank an automated tool for “Text Extraction solution” would make the quality of data and the ML model more robust.
- A mid-size insurance company was gearing up to launch a new digital insurance division, one that would focus on selling small, pocket-size insurance products to minimize focussed risks. This included small insurance products like flight-delay insurance, international travel insurance, etc. AI and Big Data models were critical for the underwriters of these products. But there was a bottleneck. The input source was often unstructured. They were in the form of pdfs, docs and images. Mining information from multiple sources and putting it into clean tables containing structured text and numbers were crucial.
The point is for most AI/ML and Big Data models in the BFSI space, building an automated text analytics process is critical.
Converting Unstructured Inputs
In the BFSI sector, there are several input data sources but they are not always easy to extract and tabulate, rendering the data difficult for analysis and decision making.
teX.ai extracts information from tables, pdfs, docs, websites and images. Our text analytics software can auto recognize the tables from documents and images and extract clean tables containing structured text and numbers. Then, the tool can export them to CSV, JSON, write it into a database in the necessary format.
teX.ai makes it easy to extract data from unconventional sources such as emails, blogs, product reviews, tweets and center logs to build big data models for several scenarios including cross-selling, up-selling, underwriting of insurance products, conceptualizing new financial products, as so on.
Automation is the only way to deliver this data extraction seamlessly. It may require customizing the tool for a particular use-case, but a manual process is an impossible task.
According to a McKinsey study, the banking industry has taken advantage of the power of automation for over two decades now. McKinsey sees the second wave of automation and AI emerging in the next few years, in which machines will do up to 10 to 25 percent of work across bank functions, enabling employees to focus on higher-value tasks and projects.
But the key to this wave will be Text Analytics. teX.ai was built keeping in mind our years of experience serving several sectors including BFSI while leveraging our recent expertise and capabilities in big data engineering, AI and Machine Learning. Also, attention to detail is critical when we build a product like teX.ai. For example, text extraction will have to happen across multiple languages. teX.ai has been built with careful observation of the finer nuances of building an automated text analytics engine. Once information is extracted and translated, it can be extracted into structured numbers and text.
Another feature that is of critical importance is data privacy. For example, say, when employers choose health insurance for their employees, they may wish to protect their privacy by not sharing certain personal information. TeX.Ai enables this through Redaction, a valuable feature that allows administrators to hide data selectively as the case may be.
Some of the other TeX.Ai capabilities include:
- Streamlining workflow, interdepartmental cooperation and client engagement by seamlessly digitizing any paper trail.
- Preparing traditionally unusable data assets into structured, query-able and analyzable data.
- Refining business Intelligence to a higher degree of accuracy and robustness.
- Enabling high accuracy text extraction from image/video.
The features of teX.Ai that make it easy to use include:
- Hosted on the Cloud; no local installation
- Options to ensure data privacy
- Adequate support model
- Regular and automatic upgrades
- Per user licensing business model making it affordable
The Opportunity in Text Analytics
A Mordor Intelligence report suggests that the global text analytics market generated USD 5.46 billion in 2019 and is now poised to grow at a CAGR of 17.35 percent to touch USD 14.84 billion in 2025. With greater demand for machine learning and big data analytics, the text analytics market across the globe is expected to expand quickly.
Right from reading forms for loan applications to e-KYC, the potential application of this tool is unlimited and can empower BFSI businesses in several ways. The tool can improve processing speed, enable the integration of big data and ensure data validation and integration with greater consistency. This can help with advanced analytics, capturing trends and opening up new avenues for business growth and even reducing risks.
Why teX.ai – AI based Text Analytics Tool
Our text analytics team integrates more than two decades of experience in the BFSI segment with technological expertise to provide businesses with insights that can fuel rapid growth. Our proprietary product teX.Ai was conceptualized, designed and built to help its clients take advantage of the power of text analytics.