The cool thing?

Its impact could be as game-changing as the internet or the advent of mobile devices. In fact, a whopping 82% of organizations either using or considering generative AI believe it will significantly change or transform their industry (source: Google Cloud Gen AI Benchmarking Study, July 2023).

What’s really shaking up the BFSI world is that any competitor can now harness and combine these AI tools for their benefit.

First off, gen AI brings a massive boost in productivity and operational efficiency. This is especially important in BFSI, where everything starts with contracts, terms of service, and agreements. Gen AI excels at sifting through and summarizing complex information, like mortgage-backed securities contracts or customer holdings across different asset classes.

But there’s more!

Foundational models like Large Language Models (LLMs) have an impressive grasp of human language and conversation context. These skills are a godsend for speeding up, automating, scaling, and enhancing customer service, marketing, sales, and compliance.

Gen AI isn’t just a tool; it’s like having a super assistant or coach for your employees. It helps them do their jobs more efficiently, freeing them up to focus on high-impact activities.

Front and Center in Finance: How Gen AI Reshapes Customer Interactions

Let’s delve into conversational finance – a specialized field where generative AI takes the spotlight. In this context, it revolves around AI-powered chatbots or virtual assistants that engage in human-like conversations using natural language processing (NLP), comprehension (NLU), and text generation (NLG).

Imagine this: generative AI models are transforming customer interactions by providing more natural and contextually relevant responses. They are trained to comprehend and mimic human language patterns, which, when applied to financial AI systems, significantly enhance the user experience.

Conversational finance is a game-changer for customers in several ways:

1. Improved Customer Support: Customers receive more accurate, engaging, and detailed interactions.

2. Personalized Financial Advice: Advice is tailored to each customer’s specific requirements.

3. Payment Notifications: Customers stay informed about their financial transactions.

Additionally, for a broader overview of the use cases of customer service operations, you can visit our article on conversational AI for customer service.

Let’s shift our focus to another area where AI shines in the banking sector: loan decision-making. AI plays a vital role in this domain, assisting banks in evaluating creditworthiness, setting credit limits, and determining loan pricing based on risk assessment. However, transparency is crucial. Both decision-makers and loan applicants require clear explanations for AI-driven decisions, especially when loans are denied, to build trust and raise customer awareness for future applications.

Here, a conditional generative adversarial network (GAN), a type of generative AI, comes into play. It is designed to generate user-friendly explanations for loan denials. By categorizing denial reasons from simple to complex, this two-level conditioning system produces explanations that are easier for applicants to comprehend

Back Office Innovations in Finance with Generative AI

Improving Accounting Operations: Financial departments harness specialized transformer models to automate auditing and accounts payable tasks. Tailored GPT models equipped with deep learning capabilities are proficient in automating various accounting processes.

1. Streamlined Document Analysis: Generative AI efficiently processes vast volumes of financial documents, extracting crucial information from reports, statements, and earnings calls, enhancing decision-making efficiency.

2. Financial Analysis and Projections: Gen AI models, drawing insights from historical financial data, forecast future trends, asset prices, and economic indicators. Based on market conditions and variables, scenario simulations offer valuable insights into risks and opportunities.

3. Automated Financial Reporting: Generative AI crafts structured, informative financial reports automatically, ensuring consistency, accuracy, and timely delivery. These customizable reports cater to specific user needs, adding significant value for businesses and professionals.

4. Fraud Detection: Generative AI generates synthetic instances of fraudulent transactions to train machine learning algorithms, enhancing accuracy in identifying suspicious activities, bolstering security, and preserving consumer trust.

5. Regulatory Requests: Banks are exploring the use of Large Language Models (LLMs) to handle simpler queries from regulators, displaying potential for efficiently responding to regulatory demands.

6. Portfolio and Risk Management: Generative AI optimizes portfolio management by analyzing historical data to identify optimal investment strategies considering risk tolerance, expected returns, and market conditions, leading to well-informed decisions and improved financial outcomes.

7. Synthetic Data Generation: Generative AI creates synthetic datasets adhering to privacy regulations, enabling financial institutions to use data for training models, conducting tests, and validation while safeguarding customer privacy.

For an in-depth exploration of synthetic data, refer to our articles comparing synthetic data and real data, or comparing synthetic data and data masking methods for data privacy.

Answering Your Financial Queries: How Generative AI Delivers Expertise

Generative AI, empowered by its expertise in understanding human language patterns and its ability to generate contextually relevant responses, takes center stage in offering precise and thorough solutions to your financial queries. These AI models can be fine-tuned using vast datasets of financial expertise, enabling them to handle a wide range of financial questions with pinpoint accuracy. They cover topics like accounting principles, financial ratios, stock analysis, and regulatory compliance. A prominent illustration of this capability is BloombergGPT, which excels in providing precise answers to financial inquiries, surpassing other generative models in the financial domain.

Source: “BloombergGPT: A Large Language Model for Finance”

Decoding Emotions: How Sentiment Analysis Elevates Finance

Sentiment analysis solutions, a component of Natural Language Processing (NLP), involves the task of categorizing texts, images, or videos based on their emotional tone, whether it is negative, positive, or neutral. This valuable tool enables companies to delve into the emotions and opinions expressed by their customers. With these insights in hand, businesses, including financial institutions, can shape strategies to enhance their services and products.

Financial institutions, in particular, can leverage sentiment analysis to:

1. 1. Assess Brand Reputation: By analyzing social media posts, news articles, contact center interactions, and various other sources, they can gauge the public’s perception of their brand.
2. 2. Evaluate Customer Satisfaction: This analysis extends to comprehending customer sentiment, aiding in the customization of services to meet customer expectations and boost satisfaction levels.

Gen AI: Redefining Value Creation for Businesses in Finance

Gen AI isn’t just another tech buzzword; it’s a game-changer for businesses. While it’s still in its early stages of deployment, the potential it holds for revolutionizing the financial services industry is immense.

The post Back-Office Operations, Risk Management, & Customer-Facing Frontiers – Is BFSI Ready for Generative AI? appeared first on Indium.

The financial landscape is an intricate ecosystem, where vast amounts of textual data carry invaluable insights that can influence markets and shape investment decisions. With the rise of Natural Language Processing (NLP) technologies, the financial industry has found a potent ally in processing, comprehending, and extracting actionable intelligence from this wealth of textual information. In pursuit of harnessing the potential of cutting-edge NLP models, this research endeavor embarked on a meticulous evaluation of various NLP models available on the Hugging Face platform. The primary objective was to assess their performance in financial text classification and summarization tasks, two essential pillars of efficient data analysis in the financial domain.

Financial text classification is a critical aspect of sentiment analysis, topic categorization, and predicting market movements. In parallel, summarization techniques hold paramount significance in digesting extensive texts, capturing salient information, and facilitating prompt decision-making in a rapidly evolving market landscape.

To undertake this comprehensive assessment, two appropriate datasets were chosen to assess models for both summarization and classification tasks. For summarization, the datasets selected were the CNN Dailymail dataset to evaluate the models’ capabilities with more general data, and a dataset of bitcoin-related articles to assess the models’ capabilities with finance-related data. For classification, the datasets selected were a dataset of IMDB reviews, and a dataset of financial documents from a variety of different sectors within the financial industry.

The chosen models for this study were:

distilbert-base-uncased-finetuned-sst-2-english

finbert

finbert-tone

bart-large-cnn

financial-summarization-pegasus

These models were obtained from the Hugging Face platform. Hugging Face is a renowned platform that has emerged as a trailblazer in the realm of Natural Language Processing (NLP). At its core, the platform is dedicated to providing a wealth of resources and tools that empower researchers, developers, and NLP enthusiasts to explore, experiment, and innovate in the field of language understanding. Hugging Face offers a vast repository of pre-trained NLP models that have been fine-tuned for a wide range of NLP tasks, enabling users to leverage cutting-edge language models without the need for extensive training. This accessibility has expedited NLP research and development, facilitating the creation of advanced language-based applications and solutions. Moreover, Hugging Face fosters a collaborative environment, encouraging knowledge sharing and community engagement through discussion forums and support networks. Its user-friendly API and open-source libraries further streamline the integration of NLP capabilities into various projects, making sophisticated language processing techniques more accessible and applicable across diverse industries and use cases.

Gathering the Datasets

In the domain of data-driven technologies, the age-old adage “garbage in, garbage out” holds more truth than ever. At the heart of any successful data-driven endeavor lies the foundation of a high-quality dataset. A good dataset forms the bedrock upon which algorithms, models, and analyses rest, playing a pivotal role in shaping the accuracy, reliability, and effectiveness of any data-driven system. Whether it be in the domains of machine learning, artificial intelligence, or statistical analysis, the quality and relevance of the dataset directly influence the outcomes and insights derived from it. Thus, to evaluate the chosen models, it was imperative that the right datasets were chosen. The datasets used in this study were gathered from Kaggle.

For classification, the chosen neutral dataset was the IMDB Movie Review dataset, which contains 50,000 movie reviews and an assigned sentiment score. You can access it here. As for the financial text dataset, the selected dataset was the Financial Sentiment Analysis dataset, comprising over 5,000 financial records with assigned sentiments. You can find it here. It was necessary to remove the neutral values since not all the selected models have a neutral class.

For summarization, the neutral dataset chosen was the CNN Dailymail dataset, which contains 30,000 news articles written by CNN and The Daily Mail. Only the test dataset was utilized for this evaluation, which includes 11,490 articles and their summaries. You can access it here. For the financial text dataset, the Bitcoin – News articles text corpora dataset was used. This dataset encompasses numerous articles about bitcoin gathered from a wide variety of sources, and it can be found here.

Text Classification

Model: distilbert-base-uncased-finetuned-sst-2-english

Link: https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english

BERT (Bidirectional Encoder Representations from Transformers) is a groundbreaking natural language processing model introduced by Google. It revolutionized the field of NLP by employing a bidirectional transformer architecture, allowing the model to understand context from both the left and right sides of a word. Unlike previous models that processed text sequentially, BERT uses a masked language model approach during pre-training, wherein it randomly masks words and learns to predict them based on the surrounding context. This pre-training process enables BERT to capture deep contextual relationships within sentences, making it highly effective for a wide range of NLP tasks, such as sentiment analysis, named entity recognition, and text classification. However, BERT’s large size and computational demands limit its practical deployment in certain resource-constrained scenarios.

DistilBERT: Efficient Alternative to BERT

DistilBERT, on the other hand, addresses BERT’s resource-intensive limitations by distilling its knowledge into a more compact form. Introduced by Hugging Face, DistilBERT employs a knowledge distillation technique, whereby it is trained to mimic the behavior of the larger BERT model. Through this process, unnecessary redundancy in BERT’s parameters is eliminated, resulting in a significantly smaller and faster model without compromising performance. DistilBERT maintains a competitive level of accuracy compared to BERT while reducing memory usage and inference time, making it an attractive choice for applications where computational resources are a constraint. Its effectiveness in various NLP tasks has cemented its position as an efficient and practical alternative to the original BERT model. DistilBERT retains approximately 97% of BERT’s accuracy while being 40% smaller and 60% faster.

Model Details:

• Parameters: 67 million
• Transformer Layers: 6
• Embedding Layer: Included
• Classification Layer: Softmax
• Attention Heads: 12
• Vocabulary Size: 30522
• Maximum Sequence Length: 512 tokens

Choosing DistilBERT for classification tasks can offer a balance between efficiency and performance. Its faster inference, reduced resource requirements, competitive accuracy, and seamless integration make it an attractive option for a wide range of real-world applications where computational efficiency and effectiveness are key considerations.

Code Snippet:

import torch

from transformers import DistilBertTokenizer, DistilBertForSequenceClassification

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained(“distilbert-base-uncased-finetuned-sst-2-english”)

model = AutoModelForSequenceClassification.from_pretrained(“distilbert-base-uncased-finetuned-sst-2-english”)

import pandas as pd

torch.cuda.set_device(0)

model.cuda()

df = pd.read_csv(dataset path)

df.head()

df.drop(df.loc[df[‘Sentiment’]==’neutral’].index, inplace=True)

X = df.iloc[:,column for sentiment evaluation]

y = df.iloc[:,target sentiment]

device = torch.device(“cuda” if torch.cuda.is_available() else “cpu”)

#metrics for the model

mydict = {‘positive’:1, ‘negative’:0, 1:1, 0:0}

count = 0

correct = 0

wrong = 0

wrong_dict = {}

for input_sequence in X:

  try:

    if y[count] == ‘neutral’:

      raise Exception(“Neutral”)

    inputs = tokenizer(input_sequence, return_tensors=”pt”).to(device)

    with torch.no_grad():

      logits = model(**inputs).logits

    predicted_class_id = logits.argmax().item()

    if predicted_class_id == mydict[y[count]]:

      correct += 1

    else:

      wrong +=1

      wrong_dict[input_sequence] = predicted_class_id

  except:

    pass

  count += 1

  print(count,’/50000 complete’, end = ‘\r’)

  # if count == 20:

  #   break

print(‘\nCorrect:’, correct)

print(‘Wrong:’, wrong)

print(len(wrong_dict))

print(‘Accuracy:’, correct/(correct+wrong))

fp = 0

fn = 0

for x in wrong_dict:

    if wrong_dict[x] == 0:

        fn += 1

    else:

        fp += 1

num_negatives = 0

num_positives = 0

for x in y:

    if x == 0:

        num_negatives += 1

    else:

        num_positives += 1

print(‘Precision:’, (num_positives-fn)/(num_positives-fn + fp))

print(‘Recall:’, (num_positives-fn)/(num_positives-fn + fn))

print(‘F1:’, (2*(num_positives-fn))/(2*(num_positives-fn) + fp + fn))

FinBERT: Specialized Financial Analysis Model

Link: https://huggingface.co/ProsusAI/finbert

FinBERT is a specialized variant of the BERT (Bidirectional Encoder Representations from Transformers) model, tailored specifically for financial text analysis. Developed by Yumo Xu and his team at RoBERTa Financial, FinBERT is pre-trained on a massive corpus of financial news articles, reports, and other domain-specific data. This pre-training process enables FinBERT to acquire a deep understanding of financial language, including intricate terminologies, domain-specific jargon, and market sentiments.

The distinguishing feature of FinBERT lies in its fine-tuning process, where it is adapted to perform specific financial NLP tasks, such as sentiment analysis, stock price prediction, and event classification. By fine-tuning on task-specific datasets, FinBERT gains the ability to extract nuanced financial insights, categorize financial events accurately, and analyze market sentiments effectively. As a result, FinBERT has proven to be a powerful tool for financial professionals, enabling them to make more informed decisions and obtain deeper insights from the vast ocean of financial text data.

FinBERT is pre-trained on a large corpus of financial text data, enabling it to learn the nuances and specific vocabulary of the financial domain. This pre-training process involves predicting missing words in sentences and is supervised using a financial sentiment dataset, which helps the model learn to classify sentiment accurately.

FinBERT Model Details

• Hidden Layers: 12
• Attention Heads: 12
• Maximum Token Input: 512
• Vocabulary Size: 30873

For more detailed information, visit: https://github.com/yya518/FinBERT

Choosing FinBERT can be a highly advantageous decision for financial text analysis due to its domain-specific expertise and fine-tuned capabilities. Unlike general-purpose NLP models, FinBERT is specifically trained on a vast corpus of financial data, granting it a profound understanding of the intricacies and nuances of financial language. This domain-specific knowledge enables FinBERT to accurately interpret financial jargon, capture sentiment nuances, and comprehend market-related events, making it an invaluable asset for tasks such as sentiment analysis, event classification, and financial news summarization.

Moreover, FinBERT’s fine-tuned nature allows it to excel in financial-specific tasks by adapting to the unique characteristics of financial datasets. Through the fine-tuning process, it learns to extract financial insights with precision, providing actionable intelligence for traders, investors, and financial analysts. By leveraging FinBERT, financial professionals can gain a competitive edge, make well-informed decisions, and navigate the complexities of the financial domain with a powerful and specialized language model at their disposal.

Code snippet:

tokenizer = AutoTokenizer.from_pretrained(“ProsusAI/finbert”)

model = AutoModelForSequenceClassification.from_pretrained(“ProsusAI/finbert”)

finbert-tone

Link: https://huggingface.co/yiyanghkust/finbert-tone

FinBERT-tone is an extension of the FinBERT model, designed to address the additional challenge of sentiment analysis in financial text. Developed by the same team at RoBERTa Financial, FinBERT-tone builds upon the foundation of FinBERT by incorporating a novel aspect – capturing the fine-grained tone of financial news articles. Unlike traditional sentiment analysis, which often focuses on binary positive/negative sentiments, FinBERT-tone aims to discern a more nuanced sentiment spectrum, encompassing positive, negative, and neutral tones.

This extension involves training FinBERT-tone on a specialized dataset that includes financial news articles annotated with granular sentiment labels. By fine-tuning on this tone-specific dataset, FinBERT-tone hones its ability to gauge the varying degrees of sentiment in financial text, offering a more comprehensive and accurate sentiment analysis solution for financial professionals. With the capability to interpret subtle sentiment fluctuations in the market, FinBERT-tone empowers users to make well-calibrated decisions and better understand the emotional aspects that influence financial events, making it a valuable tool for sentiment-aware financial analysis.

FINBERT-tone Model Details

• Fine-tuned on: 10,000 manually annotated sentences from analysis reports
• Improved Performance: Better performance on financial tone analysis tasks
• Hidden Layers: 12
• Attention Heads: 12
• Maximum Token Input: 512
• Vocabulary Size: 30873

For more detailed information, visit: https://github.com/yya518/FinBERT

This model was selected because it can prove to be a strategic advantage for financial professionals seeking sophisticated sentiment analysis capabilities. Unlike traditional sentiment analysis models, FinBERT-tone offers a more nuanced approach by capturing the fine-grained tone of financial news articles. Its specialized training on a dataset annotated with granular sentiment labels allows it to discern subtle variations in sentiment, encompassing positive, negative, and neutral tones in financial text. As a result, FinBERT-tone provides a more comprehensive understanding of the emotional undercurrents within the market, empowering users to make well-informed decisions and respond proactively to sentiment shifts.

By leveraging FinBERT-tone, financial analysts, traders, and investors can gain deeper insights into market sentiment and sentiment-driven trends. Its nuanced sentiment analysis enables users to detect shifts in investor confidence, market sentiment, and public opinion, providing a critical edge in navigating the complexities of financial markets. Additionally, the model’s fine-tuned expertise in financial language ensures accurate interpretation of domain-specific jargon and context, making it an invaluable tool for sentiment-aware financial analysis, risk management, and decision-making.

Code Snippet:

from transformers import BertTokenizer, BertForSequenceClassification

from transformers import pipeline

finbert = BertForSequenceClassification.from_pretrained(‘yiyanghkust/finbert-tone’,num_labels=3)

tokenizer = BertTokenizer.from_pretrained(‘yiyanghkust/finbert-tone’)

nlp = pipeline(“sentiment-analysis”, model=finbert, tokenizer=tokenizer, device = 0)

Continue to Part 2 Link:  Evaluating NLP Models for Text Classification and Summarization Tasks in the Financial Landscape – Part 2

Conclusion

In this first part, we’ve delved into the crucial role of high-quality datasets and explored the capabilities of foundational NLP models like distilbert-base-uncased-finetuned-sst-2-english. Understanding the significance of data and model selection sets the stage for our deep dive into specialized models tailored for financial analysis.

Stay tuned for Part 2, where we’ll explore advanced models like FinBERT and FinBERT-tone, designed to provide nuanced sentiment analysis and tone interpretation in the financial domain. These tools empower professionals to gain invaluable insights and make well-informed decisions in a rapidly evolving market landscape.

The post Evaluating NLP Models for Text Classification and Summarization Tasks in the Financial Landscape – Part 1 appeared first on Indium.

If you’ve been dealing with banks and financial organizations, chances are you’ve chatted with these super-smart virtual assistants and chatbots. The use of Natural Language Processing (NLP) in the financial sector has been on the rise worldwide. More and more financial institutions are embracing advanced tech innovations, taking NLP beyond banking, insurance, and hedge funds (especially for sentiment analysis).

Artificial Intelligence and Machine Learning, alongside NLP, are making their mark in various areas of the financial sector like, operations, risk assessment, sales, research and development, customer support, and many other fields. This expansion boosts efficiency, productivity, cost-effectiveness, and time and resource management.

Take, for instance, the convenience it brings: Instead of the hassle of logging into individual accounts to check your balance, users can now effortlessly access their account information through chatbots and voice assistants. These digital companions are everywhere, from chatbots to voice assistants like Amazon Alexa, Google Assistant, and Siri.

Sentiment Analysis, often hailed as the next game-changer in the finance sector, plays a central role in chatbots, voice assistants, text analysis, and NLP technology. It’s a key component of natural language processing used to decipher the sentiments behind data. Companies frequently employ sentiment analysis on various text sources such as customer reviews, social media conversations, support tickets, and more to uncover genuine customer sentiments and evaluate brand perception.

Sentiment analysis aids in recognizing the polarity of information (positive or negative), emotional cues (like anger, happiness, or sadness), and intent (e.g., interest or disinterest). It is crucial in brand reputation management by providing insights into overall customer attitudes, challenges, and needs. This allows for data categorization by different sentiments, resulting in more accurate predictions and informed strategic decisions.

So, how can BFSI make the most of sentiment analysis? This emerging field has firmly rooted itself in the financial industry. Banks and financial institutions can employ AI-driven sentiment analysis systems to understand customer opinions regarding their financial products and the overall brand perception.

Of course, this approach may necessitate a certain level of data proficiency that financial companies must acquire before launching full-fledged sentiment analysis projects. Sentiment analysis stands as a highly promising domain within NLP and is undoubtedly poised to play a substantial role in the future of financial services.

Here, we’ll delve into the seven most prominent applications of sentiment analysis in financial services.

1. 1. Portfolio Management and Optimization: NLP can help financial professionals analyze vast amounts of textual data from financial news and market trends to assess the sentiment surrounding specific investments. This sentiment analysis can aid in making informed decisions about portfolio management, identifying potential risks, and optimizing investment strategies.
2. 2. Financial Data Analytics: Sentiment analysis enables financial firms to gauge the market’s sentiment toward specific assets or companies by analyzing news articles, social media, and reports. This information can be used to assess the volatility of investments and make data-driven decisions.
3. 3. Predictive Analysis: NLP can be used to analyze historical data and predict the future performance of investment funds. This involves assessing sentiment and other textual data to identify high-risk investments and optimize growth potential, even in uncertain market conditions.
4. 4. Customer Services and Analysis: Financial institutions employ NLP-driven chatbots and virtual assistants to enhance customer service. These AI-driven tools use NLP to process and understand customer queries, improving customer experience and satisfaction.
5. 5. Gathering Customer Insights: By applying sentiment analysis and intelligent document search, financial firms can gain insights into customer preferences, challenges, and overall sentiments. This information is valuable for personalizing offers, measuring customer response, and refining products and services.
6. 6. Researching Customer Emotional Responses: AI-powered tools process vast amounts of customer data, such as social media posts, chatbot interactions, reviews, and survey responses, to determine customer sentiments. This allows companies to better understand customer attitudes toward their products, services, and brands and analyze responses to competitors’ campaigns.
7. 7. Credit Market Monitoring: Sentiment analysis tracks credit sentiments in the media. Financial institutions can use NLP to process information from news articles and press releases to monitor the sentiment related to specific bonds or organizations. This data can reveal correlations between media updates and credit securities’ market performance, streamlining financial research efforts.

Future of NLP – Sentimental Analysis: Where does it stand today and tomorrow?

NLP has made significant strides in the banking and financial sector, supporting various services. It enables real-time insights from call transcripts, data analysis with grammatical parsing, and contextual analysis at the paragraph level. NLP solutions extract and interpret data to provide in-depth insights into profitability, trends, and future business performance in the market.

Soon, we can anticipate NLP, alongside NLU and NLG,  being extensively applied to sentiment analysis and coherence resolution, further enhancing its role in this domain.

Training computers to comprehend and process text and speech inputs is pivotal in elevating business intelligence. Driven by escalating demand, Natural Language Processing (NLP) has emerged as one of AI’s most rapidly advancing subsectors. Experts anticipate reaching a global market value of $239.9 billion by 2032, boasting a robust Compound Annual Growth Rate (CAGR) of 31.3%, per Allied Market Research.

NLP-based sentiment analysis is an innovative technique that enables financial companies to effectively process and structure extensive volumes of customer data, yielding maximum benefits for both banks and customers. This technology is positioned to empower traditional financial institutions and neo-banks alike, as it enhances current customer experiences, diminishes friction in financial services, and facilitates the creation of superior financial products.

In the finance and banking sectors, NLP is harnessed to streamline repetitive tasks, reduce errors, analyze sentiments, and forecast future performance by drawing insights from historical data. Such applications enable firms to realize time and cost savings, enhance productivity and efficiency, and uphold the delivery of quality services.

The post BFSI’s Tech Ride with NLP and Sentiment Analysis! Chatting with Erica, EVA, Amy, and Aida. appeared first on Indium.

It can help e-marketplaces with product classification. It can help to improve product design, devise focused marketing strategies, increase operational efficiency, and much more.

Recommended: What Text Analytics Tells Us about a Customer’s e-commerce Shopping Experience

While the list of possibilities is long, the success rate is not as high. According to a Gartner study, 80% of AI projects will be unable to scale in 2020 and by 2022, only 20% will deliver business outcomes.

One of the key reasons for this failure could be not selecting the right text analytics tool that can meet business goals and scale up.

A Buyer’s Guide for choosing a Text Analytics Solution

When scouting the market for the right text analytics solution, the 5 points to keep in mind include:

1. Customization: Each organization has a different business goal and a different set of data mix to work with. Most advanced platforms use a variety of methods such as machine learning, natural language processing, business rules and topic identification to analyze data. However, these tools tend to have a fixed, black-box model approach which may generate results fast but may be ineffective in working with small data sets. Being able to see the combination of algorithms and modify them to suit your specific and unique needs is necessary for you to benefit from the tool. 
2. Accuracy of Sentiment Analysis: Human beings communicate in complex ways. Words in themselves may sometimes mean the exact opposite in a particular context. Sarcasm is a tool that conveys much but requires reading between the lines to understand it. Emojis and exclamations contribute to the meaning. So tools that merely group words to identify the sentiment as positive or negative can be widely off the mark and be misleading. Training the tool with enough data sets to be able to accurately assess the tone becomes very important for the tool to be successful. A solution such as teX.ai from Indium Software is built on a strong foundation of semantics where the tone and the other components of communication are also factored in to arrive at the meaning accurately.
3. Use of Metadata: Aiding the semantics capabilities of any good tool like teX.ai is metadata that is often ignored by many tools. This can enhance the understanding of the sentiment better and get clarity in the face of ambiguity.
4. Multi-Lingual Support: This is the age of globalization where businesses can reach out to international markets. The Internet supports people to express themselves in the language they are most comfortable with and this makes it essential for businesses to be able to tap into chatter in those languages. Text analytics focused on English alone is no longer enough and the tool should be just as proficient in the semantics of that language to be able to unearth hidden meanings.
5. Dashboards and Visualization: Are you getting only basic charts or does the tool empower you to customize reports for a better understanding of the results is a clincher. Tools with better analytics models and enhanced dashboards along with multiple visualization options can help you get a better view from your slicking and dicing of data.

Indium Software’s teX.ai is a comprehensive tool with several visualization options, an intuitive user interface, customizable algorithms providing semantics-based sentiment analysis with multi-linguistic support that can fit the text analytics needs of organizations of any size.

Relevant read: Text Analytics of Social Media Comments Using Sentiment Analysis

Are You Ready for Text Analytics?

While the tool capabilities are very important for the success of the text analytics project, it is also essential to assess your internal preparedness to derive greater success from your text analytics initiative.

• Having the Right Data sets: Your organization must have enough documentation with textual data and of the right kind to get meaningful insights.
• The Right Team: While the text analytics software can help with analytics, your team should have the capability to benefit from the data to gather actionable insights.
• Company-wide Buy-In: Any analytics initiative can provide holistic insights only with an enterprise-wide commitment to implement the changes needed to enhance customer delight.
• Speed of Implementation: The speed of transformation as well as implementing the changes based on insights will have an impact on the success of the project.

Indium – End-to-End Solution Provider

teX.ai is a SaaS product solution from Indium Software, a technology solutions company. Incepted in 1999, Indium is a ISO 27001 certified company with 1000+ team members, servicing 350+ clients across several domains. It provides customer-centric, high quality technology solutions that deliver business value for Fortune 500 and Global Enterprises.

The teX.ai solution and the experienced team with cross-domain expertise can empower you to leverage your unstructured data for insights that can accelerate growth.  teX.ai helps produce structured data, metadata & insights by extracting data from text, summarizing information and classifying content.

If you wish to implement a scalable text analytics project to transform your business, contact us now.

The post Picking the Right Text Analytics Product: A 5-Step Guide appeared first on Indium.

Facial recognition technology was always a mythical conceptthat we thought could be a tool that could solve many of our problems but would never see the light of day.

Today, facial recognition is everywhere and is a part of the everyday technology that we use.

The iPhone’s, the OnePlus smartphones, Amazon Rekognition and even Facebook is big on face recognition and image tagging.

It has moved from being a fictional concept to being a part of our everyday lives.

How does Facial Recognition Work?

Facial recognition is a variation of biometric software that can verify or identify a person through a digital image.

This is done as the software mathematically maps out their features and saves this information like a fingerprint. 

Deep learning algorithms are put to use to ensure that the individual’s identity is not mixed up and is got right every time.

In order to recognize correctly, the facial recognition software performs 3 key steps:

• Detect the face
• Scan and create fingerprints
• Match and Verify

Think of it as a visual search engine. Even in a very busy or crowded environment, the technology makes use of key factors to identify the right individual.

The applications of facial recognition can be across multiple domains and based on how you use it, it may work really well for you or may not work at all.

Let’s have a look at some of the applications of facial recognition:

1. Security and Identity Management

Countries like the US and Australia have built huge databases of their residents by registering them through their driver registration or through a compulsory registration.

The US has a database that has 117 million on it. It is one of the largest databases in the world.

This helps them keep track of the identities of their residents and seek out the illegal immigrants.

The Aadhaar initiative in India was done on the back of having a digital database.

China has facial recognition systems all over the country to prevent their residents from jaywalking at intersections.

When it comes to the question of security, facial recognition software can read expressions and emotions and can even thwart or apprehend suspicious activity.

The railways, airports and other areas of vulnerability can be kept safe with facial recognition.

2. Retail / Emotion & Sentiment Analysis

Systems today accurately show the facial expression detection and can measure key emotions frame by frame.

Along with this attention, engagement and consumer sentiment (positive/negative) can be measured.

Human emotion is recognized from facial expressions by these emotional recognition systems.

Take the case of a retails store, repeat customers can be recognized by the software and prompt the sales people to greet them by name, thereby enhancing their shopping experience.

3.How Marketing Benefits

We humans have the ability to distinguish clearly between two individuals.

Computers’ being able to do the same thing has been taken a step forward as they now tell us what catches the eye of a customer in a mall, a shop or even in a public space.

Tailored marketing messages can be created for these spaces once customer segmentation is done This can be put to use even in the digital space.

Conducting a market research with a pre-determined sample size where the participant’s expressions while browsing web pages or reading advertisements on the web can be monitored.

This however will need to take place in a natural environment to yield good quality data.

4. In Healthcare

One of the greatest advantages of facial recognition is that it is contactless. This enables a wide range of applications to be possible.

Identity management using genetic screening while checking into clinics, hospitals or other medical facilities becomes extremely easy.

Authorization of transactions by insurance companies also becomes easier and much faster as the medical records can be fetched in seconds.

Bringing telemedicine to the world can be a possibility as facial recognition can power emotion and sentiment analysis to work in mental healthcare environments.

5. The World of Finance and Authentication

Facial recognition can most definitely disrupt the point of sale payments, there would be no swiping of the cards and no waiting in queues.

For the financial services industry, this would be a huge boon. Customers today are demanding digital solutions that are customer-centric.

The need of today’s customer is to ensure security and deliver maximum customer satisfaction. As selfish as that may sound, facial recognition is the answer to it.

Facial recognition can be used as second factor of authentication in mobile banking. ATM’s can use facial recognition for faster cash disbursement.

In cases of senior citizens who need to provide proof of life to be eligible for pension, facial recognition would save the day.

A Few Things to Bear in Mind:

An advanced analytics technique as promising as facial recognition will definitely have a few glitches.

Today, this technology leaves us wanting more in terms of accuracy.

It hasn’t been tested to the level that is expected which leaves us thinking as to when it may malfunction.

Another issue with facial recognition may be privacy and consent.

There are millions of laws across human rights, technological privacy and a lot more that need to be taken into account before implementing something like this.

Acquiring consent while introducing this technology may be difficult.

However with databases of Snapchat, Facebook and LinkedIn being at 200 million, 3 billion and 467 million respectively, this concern may not be insurmountable after all.

Facial recognition is just like how we used to talk about AI a few years back.

Facial recognition software’s today are in fact powered by AI. The technologies that come out are not mutually exclusive but are actually built on top of one another.

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