How Text Analytics enables the BFSI sector with effective information extraction

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 services has become a key part of this process.

Consider these recent examples to showcase the power of text analytics solutions 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 tool. 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 solutions 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.

Automating Text Extraction

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 tool. 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 services 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 tool. 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 Software

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.

Text Analytics Approaches which makes Business decision Easier

It is safe to say that you are accepting more criticism than you would ever peruse, not to mention sum up? Possibly you've utilized Text Analytics solution is to examine freestyle printed criticism?

These strategies go from basic procedures like word coordinating in Excel to neural systems prepared on a huge number of information focuses.

What is Text Analytics?

Text examination is the way toward separating significance out of text. For instance, this can be dissecting text composed by clients in a client review, with the attention on discovering basic topics and patterns. The thought is to have the option to inspect the client criticism to advise the business on making key move, so as to improve client experience.

What is Text Analytic Software?

To make text examination the most effective, associations can utilize text investigation programming, utilizing AI and normal language handling calculations to discover significance in colossal measures of text.

5 Text Analytics Methods

1: Word Spotting

The scholastic NLP analytics solutions people group doesn't enroll such a methodology, and which is all well and good. Truth be told, in the scholarly world, word spotting alludes to penmanship acknowledgment (spotting which word an individual, a specialist maybe, has composed).

There is likewise watchword spotting, which centers around discourse handling

The fundamental thought behind content word spotting is this: If a word shows up in text, we can accept that this bit of text is "about" that specific word. For instance, if words like "cost" or "cost" are referenced in a survey text extraction solutions, this implies this audit is about "Cost".

2. Manual Rules

The Manual Rules approach is firmly identified with word spotting. The two methodologies work on a similar guideline of making a match design, however these examples can likewise get very mind boggling.

For instance, a manual standard could include the utilization of normal articulations – something you can only with significant effort actualize in Excel. Here is a standard for allocating the class "Staff Knowledge" from a famous venture arrangement Medallia:

3. Text Categorization

How about we carry some clearness to the untidy subject of Text Analytics solutions, the manner in which it's pitched by different sellers and information researchers.

Here, we'll be taking a gander at Text Categorization, the first of the three methodologies that are really computerized and use calculations.

The magnificence of text arrangement is that you just need to give models, no manual formation of examples or rules required, not at all like in the two past methodologies.

4: Topic Modeling

Theme displaying is additionally a Machine Learning approach, however an unaided one, which implies that this methodology gains from crude content. Sounds energizing, correct?

Once in a while, I hear bits of knowledge experts allude to any Machine Learning approach as "subject demonstrating", however information researchers generally mean a particular calculation when they state theme displaying.

It's called LDA, an abbreviation for the tongue-curving Latent Dirichlet Allocation. It's an exquisite scientific model of language that catches points (arrangements of comparative words) and how they range across different writings.

Approach 5. Topical Analysis

The entirety of the previous methodologies referenced have inconveniences. In the best case, you'll get OK results simply in the wake of spending numerous months setting things up. What's more, you may pass up the obscure questions.

The expense of acting late or passing up significant bits of knowledge is colossal! It can prompt losing clients and stale development. This is the reason, as per YCombinator (the startup quickening agent that created more billion dollar organizations than some other), "at whatever point you aren't dealing with your item you ought to be addressing your clients".

I accept an ever increasing number of text analytics companies will find Thematic Analysis, on the grounds that dissimilar to every single different methodologies, it's a straightforward and profound examination that doesn't require preparing information or time for making manual principles. These are the ways to deal with simpler the procedure of text examination arrangements.

Guide to Gain a Valuable Insights through Voice of Customer

Text Analytics Solutions empowers organizations to all the more precisely decipher what their clients are stating, their needs and needs, how well those requirements are being met, and what new items, highlights, or administrations may speak to them.

It is the time of social coordinated effort, associations today are not, at this point constrained by the physical dividers, the practical fringes, or the back to front perspectives; clients and accomplices are additionally including through social commitment and joint effort in the business environment. Also, being client driven is the key need for some ground breaking organizations in the advanced time, henceforth, text analytics solutions needs to turn into the standard culture subject. In fact, how to pick up client sympathy through client slant investigation? All the more explicitly, how might you examine and structure a ton of text content from clients?

A Voice of Customer - VOC arrangement should be incorporated into a more extensive client experience technique. Simultaneously, it's a disgrace not to deal with all significant input that is accessible at the fingertips. There are various programming arrangements out there that are explicitly committed to client supposition classification and catchphrase examination, etc, and it bodes well to have text analytics tool coordinated completely inside a more extensive client experience the board arrangement. Tragically, most organizations need to accomplish the difficult work and read the free text comments, experiencing each and every one. You can generally utilize a sifting, for example, including rating inquiries in the studies along the text analytics solutions, with the goal that you just read, break down and classify the ones that issue generally, for example, Detractors, or clients that for reasons unknown worth high one perspective and low another and need an inside and out investigation.

Find the underlying driver of client's input. Another technique you could utilize is to ask the client his/her principle motivation to give you the input. Here, you really approach the client for the underlying driver of his/her feedback.This has the upside of objectivity (of understanding) of workers organizing the criticism in addition to the way that you are increasingly effective in the text analysis solutions of input. Also, you realize where to search for development prospects. For instance a client said he/she visited the store and needed to trust that 30 minutes will be served. The worker was neighborly, yet not expert dynamic... Underlying driver prospects inside the study: holding up times, nature of administration, invitingness. In the event that the client answers "nature of administration" you have your first underlying driver. You might need to utilize a subsequent main driver: Proactivity, Know-How For this situation, a client would state proactivity.

The text analytics tool or the mix of approaches can make client nostalgic examination increasingly powerful. There're various programming and endeavor arrangements out there for this reason and dissecting client criticism is something other than remarks from a review. You can utilize a blend of manual read through and text analytics software to sort the remarks (single words, statements, phrases, and so forth.) versus the focused on qualities. You would then be able to utilize distinctive text analytics tool to break down the key remarks, how they are bunched, and how they are connected to the client experience. It takes in your review information and presents outlines and diagrams that show assessment across text classification solutions.

Responsibility for reviews inside an association ought to be taken care of halfway; to guarantee a technique and great quality, along with the advancement required dependent on client input. Numerous organizations battle with text analytics tool of how to effectively function with client experienced-based dynamic. It will be a test to discover a venture arrangement without a typical procedure and various progressing specially appointed arrangements. Responsibility for overviews ought to be concentrated or facilitated halfway, in any case there is peril of the client getting numerous reviews from various divisions winding up in study exhaustion. correspondingly, the yield and activities coming out of the reviews ought to be facilitated midway, else, you end-up with capacities working experiencing some miscommunication in light of a legitimate concern for improving the utilitarian level scores. So the responsibility for client study control unquestionably has a place with a focal spot, above all for client contact control, yet in addition to authorize consistency and give aptitude which will drive more prominent appropriation of the activity over the endeavor. When the investigation is characterized, responsibility for viability (Closed Loop goals, significant customer activity plans) live with the claiming division or support. The focal group screens the submitted activity plans by division and recommends coordination when like prerequisites exist across divisions or geologies.

It takes investigation thinking to do client text analytics solutions bit by bit:

1) Generate high-recurrence phrases in text analytics. This progression will give you patterns and high-recurrence text in your client overviews. It will likewise give you patterns and some understanding into client experience.

2)Define word reference dependent on the high-recurrence phrases. You have to make vocabulary/word reference of words to sum up the data.

3) Create Customer Journey Chart and partner this word reference with client excursion to distinguish the region of agony focuses and make remedial move.

4) The above advances will give you data, however to construct noteworthy bits of knowledge, you have to do some text analysis solutions the key remarks and point out the torment regions and activities required.

5) Availability of open source programming has helped in producing this data at exceptionally insignificant speculations. You can make every one of these means utilizing the product and with low manual endeavors, you can achieve in bits of knowledge the overviews.

Much the same as a scientific, the key point here isn't innovation yet to produce the bits of knowledge, which can help you in improving the client experience and by and large give an edge over your rivals. Doing Sentimental Analytics solutions is the way to investigate what's in clients' psyche, gain client sympathy, and make a move for building a more client driven business.

Thanks,
Charles,
teX-Ai - Text Analytics Software | Sentiment Analysis Tool