Custom Software Development | Applied AI Consulting

Automating Rule Generation with Generative AI for a Leading Offer Management Platform in Financial Services.

Thu, 22 Aug 2024 11:27:45 +0000

Automating Rule Generation with Generative AI for a Leading Offer Management Platform in Financial Services

Accelerating Code Generation, Boosting Accuracy, and Streamlining Payout Processes for Enhanced Efficiency in Financial Offer Management

Company Overview

A leading company in the offer management platform domain for banks and financial institutions, this organization provides innovative solutions to streamline the verification and fulfillment of offers. Their platform enhances customer engagement by managing offers efficiently and ensuring compliance and accuracy in payouts. Known for their cutting-edge technology, the company serves a diverse clientele, helping financial services businesses achieve their marketing and operational goals.

Issue

The process of manually writing JavaScript code to verify offer fulfillment was inefficient, leading to time-consuming tasks and a high risk of errors.

The lack of an automated solution to generate JavaScript code based on customer and offer metadata created bottlenecks in the workflow.

The accuracy and reliability of the manually generated code were inconsistent, potentially causing delays in payouts.

Let’s discuss your use cases

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AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

Developed a Generative AI-based solution that automated the generation of JavaScript code, significantly reducing manual effort and errors.

Implemented natural language processing (NLP) to enable the system to accurately interpret customer and offer metadata for precise code generation.

Integrated the AI solution with the existing offer management platform, ensuring real-time and accurate code output. Designed a user-friendly interface to make the process intuitive and accessible for the customer success team.

Impact

Accuracy and Reliability

Enhanced the accuracy and reliability of the generated code.

Productivity Boost

Significantly reduced the time required to generate and verify offer fulfillment code and hence improved the overall efficiency of the customer success team.

Faster Payouts

Enabled quicker and more accurate payouts based on verified offer fulfillment.

Driving Quality and Reliability for Modern Mobile and Web Applications

Wed, 21 Aug 2024 15:26:41 +0000

Driving Quality and Reliability for Modern Mobile and Web Applications

Building a Modern Mobile and Web Application with Robust CI/CD Automation and AI-Powered Multi-Device Testing

Company Overview

A leading start-up in the online consulting platform domain, this company focuses on connecting end-users with top industry professionals for expert consultation services. Known for its innovative approach and cutting-edge technology, the company aims to provide seamless and efficient solutions for both consultants and clients, ensuring high-quality interactions and problem-solving experiences. Applied AI Consulting has created the modern stack with payment gateway integration for this customer. This case study is focused on quality engineering.

Issue

Cross-Platform Compatibility: Building a modern application that supports both mobile and web platforms can introduce challenges in ensuring consistent functionality and user experience.

Testing Coverage: Ensuring thorough and reliable UI and API testing to cover all possible use cases and scenarios, which is crucial for maintaining application quality and reliability.

Automation Integration: Integrating automation testing into the CI/CD pipeline effectively, ensuring that automated tests run seamlessly with each deployment to catch issues early.

Performance and Reliability: Maintaining high performance and reliability of the application across multiple platforms and under varying load conditions.

Let’s discuss your use cases

Contact Sales

Similar Use Cases

What Is an AI Maturity Model?

A maturity model is a framework used to assess how well your organization uses a

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

Developed and Integrated Automation Frameworks: Created a Selenium-Python-based framework for UI testing and a pyTest framework for API testing, integrating them with the CI/CD pipeline.

Executed Cross-Platform Testing: Utilized aiTest for automation across multiple devices, browsers, and versions, including API quality validation with Postman collections.

Enhanced Communication and Visibility: Provided test execution updates and analytics through Slack and email, and demonstrated automation results in end-of-iteration demos.

Impact

Enhanced Quality and User Experience

Achieved very high application quality with minimal defects and ensured a seamless user experience across devices and browsers.

Accelerated Release Cycles

Reduced testing time by 40% through CI/CD integration and automation, leading to faster feature releases and updates.

Cost Savings and Performance Improvement

Decreased testing-related costs by 30% and improved application performance, contributing to higher user retention and scalable growth.

Customer Testimonials

Applied AI Consulting has been instrumental in driving the quality and reliability of our mobile and web applications. Their expertise in building a modern stack with seamless payment gateway integration has allowed us to provide our users with a smooth and efficient experience. The team's commitment to excellence and their innovative approach have significantly enhanced our platform's performance, ensuring we meet the high standards our clients expect.
Founder Click to Connect

Building a Comprehensive Online Consulting Platform with Payment Integration for a Leading Start Up

Wed, 21 Aug 2024 12:48:08 +0000

Building a Comprehensive Online Consulting Platform with Payment Integration for a Leading Start-Up

Enhancing User Experience with Secure Payments, Real-Time Notifications, and Automated Deployments

Company Overview

Issue

Ensuring high availability and scalability of the mobile and web applications.

Integrating a reliable and secure payment gateway.

Automating the deployment processes to reduce downtime and improve efficiency.

Maintaining high performance and user experience during peak loads.

Let’s discuss your use cases

Contact Sales

Similar Use Cases

What Is an AI Maturity Model?

A maturity model is a framework used to assess how well your organization uses a

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

Successfully deployed the ClickToConsult mobile application on both the App Store and Play Store, leveraging Firebase for push notifications and AWS Lambda for backend processes, ensuring scalability and cost efficiency.

Implemented a robust web-based interface for managing administrative activities, with integrated Razorpay payment gateway for secure transactions, providing a seamless experience for users and administrators.

Set up CI/CD pipelines for automated deployments across the mobile and web platforms, minimizing downtime and facilitating smooth, efficient updates.

Impact

Launch of a Scalable Consulting Platform

Successfully launched a scalable and high-performing online consulting platform, ensuring seamless interactions and enhanced user experience.

Secure Payments and User Trust

Integrated a secure and reliable payment gateway, boosting user trust and satisfaction through secure transactions.

Cost Efficiency with Serverless Architecture

Achieved significant cost savings and operational efficiency by utilizing serverless architecture and automated deployments.

Driving Quality and Reliability for Modern Mobile and Web Applications: A Case Study for a Leading Online Consulting Platform

Tue, 20 Aug 2024 15:53:13 +0000

Driving Quality and Reliability for Modern Mobile and Web Applications: A Case Study for a Leading Online Consulting Platform

Developed a modern app with mobile and web support, integrated automated testing using Selenium, pyTest, and aiTest, and validated API quality with Postman for consistent, reliable results.

Company Overview

A leading start-up in the online consulting platform domain, this company focuses on connecting end-users with top industry professionals for expert consultation services. Known for its innovative approach and cutting-edge technology, the company aims to provide seamless and efficient solutions for both consultants and clients, ensuring high-quality interactions and problem-solving experiences. Applied AI Consulting created the modern stack with payment gateway integration for this customer, emphasizing quality engineering to ensure reliability and performance.

Issue

Building a modern application with mobile and web support for their consulting platform.

Ensuring application reliability and quality through comprehensive UI and API testing.

Integrating automation testing into the CI/CD pipeline to streamline releases and improve efficiency.

Let’s discuss your use cases

Contact Sales

Similar Use Cases

What Is an AI Maturity Model?

A maturity model is a framework used to assess how well your organization uses a

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

Agile Collaboration and Automation Framework Development: Collaborated closely with the development team in an agile environment to create test cases and architect a selenium-python-based UI automation framework and a pyTest-based API automation framework.

Advanced Testing with aiTest and CI/CD Integration: Utilized aiTest for executing automated tests across various devices and browsers, integrated with CI/CD pipelines to ensure consistent testing across QA, pre-prod, and prod environments, and validated API quality using Postman.

Real-Time Communication and Iteration Demos: Provided real-time updates on test execution and analytics via Slack and email, and conducted end-of-iteration demos to showcase automation progress and results.

Impact

Email Optimization

Modern App Development with Automation Integration: Successfully built a modern application supporting both mobile and web platforms, integrating an automation suite into the CI/CD pipeline for consistent and reliable testing.

Productivity Boost

Robust Testing Frameworks: Developed a selenium-python-based UI automation framework and a pyTest framework for API automation, ensuring comprehensive and dependable test coverage.

Scalable Infrastructure

Cross-Platform Testing and API Validation: Leveraged aiTest for automated testing across multiple devices and browsers, and integrated Postman for API quality validation and scalable testing.

Customer Testimonials

Applied AI Consulting has been instrumental in transforming our development and deployment processes. Their innovative approach to integrating automation into our CI/CD pipeline has allowed us to scale efficiently while maintaining top-tier application reliability. The cost savings and operational efficiency gains we've experienced have been profound, enabling us to focus more on strategic growth. Their ability to seamlessly blend technical expertise with a deep understanding of our business needs has made them an invaluable partner.
VP of Engineering

Transforming Financial Services with AWS: Capital Market Platform Journey to Enhanced Scalability and Efficiency

Tue, 20 Aug 2024 15:12:34 +0000

Transforming Financial Services with AWS: Capital Market Platform Journey to Enhanced Scalability and Efficiency

Capital Market Platform Journey to Enhanced Scalability and Efficiency

Company Overview

A leading financial services company focused on global emerging markets, this organization delivers up-to-date market insights from major financial centers to local investors. Known for its intelligence-driven approach, the company provides valuable information to help investors navigate diverse market conditions effectively. The company is committed to leveraging advanced technologies to enhance its services and maintain a competitive edge in the financial sector.

Issue

Migrating and modernizing the content management application to AWS.

Implementing AWS cloud operations and DevOps for continuous integrations and deployments.

Enhancing scalability and reliability of the applications.

Setting up efficient monitoring and operations dashboards.

Let’s discuss your use cases

Contact Sales

Similar Use Cases

What Is an AI Maturity Model?

A maturity model is a framework used to assess how well your organization uses a

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

Containerized and Scalable Deployment: Containerized the EMFI application using AWS ECS Fargate and set up an ALB for traffic distribution and auto-scaling.

Automated CI/CD and Infrastructure Management: Implemented CI/CD pipelines, automated environment deployments with ASG, and managed AWS services using Terraform.

Enhanced Monitoring and Migration: Established a Datadog dashboard for monitoring and facilitated migration from private GitLab to public GitHub.

Impact

Cloud Migration and Modernization

Successfully migrated the content management application to AWS, modernizing its infrastructure.

Scalability and Reliability

Achieved high scalability and reliability through containerization and automated deployments.

Improved Monitoring and Collaboration

Enhanced system visibility with Datadog and streamlined version control by migrating to GitHub.

Customer Testimonials

Applied AI Consulting’s expertise in building custom AI solutions with ML and NLP, along with their scalable AWS implementation, significantly enhanced our product’s performance. Their innovative approach and commitment to quality have driven tangible improvements, greatly benefiting our customers.
CTO 7Targets.ai

Nine NLP Tools to help you get started with NLP today

Mon, 31 Oct 2022 07:27:13 +0000

Every day businesses generate a mountain of data; it is an inevitable result of almost every business interaction—emails, websites, blog posts, whitepapers, internal documentation, reports…the list is endless. But this is not clutter. There are valuable insights to be mined from this mountain of data. But analyzing this unstructured data near overwhelming for humans to sort through, and it’s just as hard for computers to understand. Enter NLP. A subset of artificial intelligence, NLP leverages linguistics and computer science to make human language intelligible to machines. By enabling computers to automatically analyze massive sets of data, NLP can sort through massive data sets to unearth meaningful information in just seconds. NLP can conduct various text analyses, including sentiment analysis, topic classification, and more.

Best of all, you don’t need to build an NLP application from scratch. There are several NLP tools that are available through SaaS models and open-source libraries that can be implemented easily.

If you’re looking to hit the ground running, SaaS tools are for you. These tools are ready-to-use, mostly cloud-based solutions that need little to no code to be implemented.

Pre-trained NLP models on SaaS platforms are ideal for those looking for a code-free solution; Professional developers—anyone with the ability to code, really—and those who want a flexible, low-code, option to simplify their work can leverage the APIs provided by SaaS platforms.

For those users looking for full customization of their NLP tools, there are open-source libraries. These are free, flexible, and allow major customization of your NLP tools.

As open-source libraries are aimed at developers, they can be fairly complex to grasp and users will need experience in machine learning to build open-source NLP tools.

Fortunately, most of these frameworks have support communities, so you can count on help when and if you need it. Ready to get started with using NLP, here are some powerful online tools that can help.

1. BERT

Bert—an acronym for Bidirectional Encoder Representations from Transformers—is an open-source machine learning framework, designed to help computers understand the meaning of ambiguous language in text. Bert works by using the text surrounding the problematic text to establish context. BERT framework was pre-trained using Wikipedia content and can be further fine-tuned with Q&A datasets.

BERT is based on a deep learning model called Transformers wherein each output element is connected to an input element, and the weightage between them is dynamically calculated based on their connection. Unlike traditional language models that only read text input sequentially, viz left-to-right or right-to-left, BERT is designed to read in both directions at once. This is described as bi-directionality.

Using bidirectionality, BERT is pre-trained on Masked Language Modeling and Next Sentence Prediction.

Masked Language Model (MLM) is used to predict a masked word based on the hidden word’s context. Next Sentence Prediction can predict whether two given sentences have a logical, sequential connection or whether their relationship is simply random.

2. IBM Watson

IBM Watson is an offering by IBM cloud. It comprises a suite of AI services, which are stored in the IBM Cloud. One of its key features, Natural Language Understanding, identification, and extraction of keywords, categories, emotions, entities, and more.

IBM Watson is versatile. It can be tailored to the needs of different industries, from HLO to Fintech, and has a trove of documents to help you get started.

3. Google Cloud Natural Language API

Google Cloud Natural Language API uses pre-trained models to run sentiment analysis, content classification, entity extraction, etc. Google Cloud also enables the building of bespoke ML models using AutoML Natural Language. Google Cloud Natural Language API is an element of Google’s Cloud infrastructure. it offers several benefits, including

Using Entity analysis to identify and label fields within documents, such as emails, chats, social media posts, and interactions, and run sentiment analysis to map customer expectations. Users can also use the Speech-to-text API to extract insights from audio content, while Vision API brings optical character recognition for scanned documents

To get you started on your NLP journey, Google Cloud’s Natural Language AI even gives new customers USD300 in free credits to use on Natural Language, Plus 5,000 units for analyzing unstructured text every month, which is free, i.e. not adjusted against your credits.

4. Amazon Comprehend

Amazon Web Services too offers an NLP service, known as Amazon Comprehend. It is integrated with the Amazon Web Services infrastructure. This API can be used for NLP tasks such as sentiment analysis, topic modeling, entity recognition, and more.

There’s also a separate variant for the healthcare industry: Amazon Comprehend Medical. It allows you to perform advanced analysis of medical data using Machine Learning.

5. SpaCy

SpaCy is one of the latest open-source Natural Language Processing services using Python libraries. It’s exceedingly fast and easy to use. SpaCy is supported by detailed documentation and can handle large data volumes. It also boasts a series of pre-trained NLP models that make your job even easier.

SpaCy makes it easy to select the best algorithm for each task; it simply serves up the best available option. Bu keeping its menu short and displaying the best-fit option, it saves you the task of work of going through a large menu of algorithms every time you want to run a certain task.

SpaCy’s library is a great option to prepare text for deep learning and extraction tasks. But only in English…as it is currently only available in English.

6. NLTK (Natural Language Toolkit)

NLTK is a Python library and another leading tool used to build NLP models. NLTK has built a large and active community, and it also offers several tutorials for language processing and sample datasets. Users can also avail of several resources provided with the toolkit, including a comprehensive Language Processing and Python handbook.

NLTK’s library does have a longer learning curve but it’s considered an amazing sandbox to obtain hands-on NLP experience. It has a modular structure and provides tools to conduct NLP tasks like tokenization, tagging, stemming, parsing, and classification, among others.

TextBlob

Like NLTK, TextBlog too is a Python library; in fact, it is built to work as an extension of NLTK. TextBlog lets you tackle NLP tasks like sentiment analysis, text classification, part-of-speech tagging, and more. It has an intuitive, user-friendly interface that makes it simpler for users to perform the same NLP tasks (as NLTK), making it an excellent choice for beginners.

Stanford Core NLP

The prestigious Stanford University’s NLP community gets the credit for building and maintaining Stanford Core NLP. The tool is built using Java; which means users need to install JDK on their computers to use it. However, it provides APIs in most programming languages.

Stanford Core NLP toolkit enables users to perform a variety of NLP tasks, including tagging (parts of speech), tokenization, and named entity recognition, among others. It is built for scalability and speed, making it a good choice for complex tasks.

Gensim

Gensim is a specialized Python library. It is mostly used to handle topic modeling tasks, for which it uses algorithms like Latent Dirichlet Allocation (LDA). Gensim excels at recognizing text similarities, texts indexing, and navigating different documents. It is fast, scalable, and can handle large volumes of data with

Ten ways NLP is adding value to real-world business operations

Mon, 31 Oct 2022 07:11:22 +0000

Natural Language Processing (NLP) has become a real game changer for businesses—some more than others. But no matter what your business or sphere of work, there are many ways in which NLP can help ease your workload, improve accuracy, and save money. Here are ten real-life use cases for NLP in business.

1. Document (Text) Classification

Text is everywhere: emails, chats, website content, social media posts, blog posts…there’s a lot of it out there and it’s mostly unstructured, making it hard to extract any kind of value. Which is where NLP comes in. Text classification or text categorization is simply the process of segregating text into specific groups. NLP automates this process and assigns pre-defined tags to each based on the content of each document. This makes the documents easier to manage, sort, and mine for specific information. For instance, say you receive a mountain of emails every day. It’s all mixed up and you can’t risk deleting it en masse for fear of throwing out the baby with the bathwater. NLP can classify these documents in a flash, by separating them into various categories, based on the occurrence and frequency of certain keywords. Text that has keywords pertaining to more than one category can simply be categorized under both classes.

2. Sentiment Analysis

Every time we use emojis to register our approval (or dislike) of certain posts on social media or post reviews of a product or service we are engaging in expressing our sentiments. This is invaluable for brand/business owners, who use it to inform their business/brand strategies. But sentiment analysis can go far beyond this. For instance, it can be used to determine market/stock trends, based on the frequency of certain keywords.

Sentiment analysis can also be used by HR departments—using posts, feedback surveys, reviews, etc.—to gauge employee satisfaction with their job roles and strategize accordingly to enhance attrition.

3. Data Mining

Every day we generate a massive volume of data, most of it unstructured, but no less valuable. To unearth this intelligence, however, it is necessary to sift through tomes of text—next to impossible to manage manually, but a breeze for NLP. NLP solutions can automate the extraction of specific data related to a particular topic from any source, be it text documents, databases, or websites.

NLP can be used to train models to classify documents according to such variables as ‘date’, ‘person’, geography’, organization, or an ‘event’. The Fintech industry, for instance, uses NLP models to extract data from statements, reports, claims, and forms. This reduces the chances of error associated with manual entry, increases accuracy, and automatically sorts documents according to custom rules.

4. Chatbots

NLP can give chatbots conversations near human sentience

Who amongst us hasn’t thrown up their hands in exasperation after a frustrating session with a chat application? Conventional chatbots—even those personalized with names, and anthropomorphic features—are just not up to the task of conversing with humans. Enter NLP. Chatbots built using NLP solutions are now almost sentient. They can understand intent, converse in context and learn from previous conversations, making each interaction better than the one before. Bot models can be built and trained to perform specific conversations without needing to collect and tag extensive datasets for their training. The uses for chatbots, and voice assistants is seemingly limitless, from conducting interviews for HR to performing certain actions like Siri does. And since it is designed to take an automatic learning approach, it just gets better the more you use it.

5. Text Summarization

Typical process of automated text summarization

Information overload and Content Shock are very real. The continuous flood of information can be hard to process or use effectively. Summarization allows you to condense this voluminous information into convenient formats that are easy to digest. Summarization can be particularly helpful in the following cases:

Media Monitoring
RSS feeds
Website content—with a view to SEO.
Internal knowledge—typically stored as unstructured data
Financial research—earning reports, financial news, et al.
Contract analysis
Repurposing long-form content for social media marketing
Email—enabling us to skim emails quickly

Like to know how summarization can help you? Ask us in the comments below, or reach out to our NLP experts.

6. Custom search applications

Nobody (well, almost nobody) really knows for certain how Google’s algorithms might change in the future, but one thing is certain: NLP will play a big role in redefining how search engine marketing works. With NLP, search queries can better understand the intent behind the search…for instance homophones like accept and except; or homonyms like right (as in correct) and right, as in the opposite of left. The implication for SEO marketers is clear: content is doubly important now, as in content that is designed for human consumption rather than search bots.

For business organizations, search engines play an important role too. They help search emails, documents, etc. When searching for anything using any query, the search widget creates a list of documents matching the query. And it uses an index of documents/vocabulary to do this. Enterprise search engines that are built using NLP models will not face typical problems such as longer search queries or words that have dual meanings. A custom search engine is thus able to quickly and accurately find documents according to the searcher’s real intent.

7. Text similarity

Plagiarised content is a huge problem, not just in academia but in everyday writing as well—this article, for instance. Using NLP it is easy to build applications that flag documents that show a high degree of similarity—not merely by matching words, but by searching for semantical similarities, too.

8. Language translation

The idea of machine-translation has always been around; the challenge lay in the complexity inherent in any language. Then in the late 1990’s Alta Vista—a search engine that was later bought and merged into Yahoo—launched the world’s first web-based translation tool: Babel Fish. While its translations do present users with a gist of the translated material, it is far from perfect. It doesn’t play well with idioms and is often confounded by homonyms and other figures of speech. Thus language translation remained a dream until Google launched Google Translate. Google Translate, which originally used statistical machine translation, now uses neural machine translation—translating entire sentences at a time, rather than just piece by piece. This allows it to use a broader context to identify the most relevant translation, in effect, allowing it to speak almost like a human and in real-time.

NLP-powered language translation allows you to translate instantly translate content pieces, such as web pages, into selected languages with amazing accuracy.

9. Conversational AI

Conversational AI is the term describing the technology that is at the heart of virtual assistants like Cortana, Alexa, and Siri. These applications rely on NLP to understand the intent behind user queries and generate an accurate response. Such chatbots are immensely useful in answering customer queries, and even assisting in rule-based tasks like booking flights. Their accuracy and 24×7 availability spell lower costs, and higher user engagement. They can be used to excellent effect in reducing workloads on call centers, allowing customer service employees to focus on more complex issues.

Automated Speech Recognition (ASR)

ASR works by converting human speech from analog acoustic sound waves to digital forms that can be read by machines. It splits speech into individual sounds—called tokens—analyzes each sound, and uses NLP + deep learning algorithms to identify the best-fit word in the selected language, before converting the sound into text. Many Smartphones today offer speech recognition applications (Siri, for example) to enhance accessibility beyond texting.

Five Industries that NLP Has Changed Forever

Thu, 27 Oct 2022 07:17:24 +0000

The advantages of having machines perform human actions are quite obvious, and language is one area that has attracted scientists and developers for a long time. The ability to let machines take over certain tasks does more than save us the drudgery of doing it ourselves or employing someone else to do it. It eliminates manual errors, ensures consistency, saves time, and is cost-saving too. Since the early efforts in the 50’s, the art of processing language, or Natural Language Processing (NLP) as we know it, has come a long way and is a subset of Artificial Intelligence that many industries are leveraging in their systems of processes. Almost every sphere of human activity can benefit from NLP, but the following five industries are already at the forefront of NLP usage. Briefly, they are HLS, Retail, Fintech, HR, and Cyber Security.

This article takes a deeper dive into each to understand how each benefits from NLP.

(For a clearer understanding of NLP, read our article: NLP—how it works, and how it benefits you.)

Healthcare & Life Science (HLS)

Medical transcription

Physicians typically dictate processes to a voice recorder. This is a good time saver but the audio file still needs to be converted into text by a human. This makes it prone to human errors, and inconsistencies, and is a cost factor. NLP eliminates all three.

Clinical documentation

On average, healthcare workers spend >4 hours a day entering data into Electronic Health Records (EHR). NLP, in combination with Optical Character Recognition (OCR), can extract specific healthcare data from the EHR or the physician’s notes and feed it directly into a data entry application, for instance, an RPA bot. This is a massive time saving and also improves the data quality by eliminating human errors.

Clinical trials

NLP is also used to interpret text—doctors’ notes, pathology reports, etc.—to identify individuals eligible to participate in a given trial. The NLP algorithm is trained on medical records and research papers to hone the ability to recognize medical terms, understand the general needs of the trial, generate a list of qualifications needed, and then evaluate participants’ applications.

Clinical diagnosis

NLP is being used to create medical models that can recognize disease criteria based on clinical terminology and suggest treatment recommendations.

Virtual therapy

Virtual therapists are a form of conversational AI in healthcare that is being successfully used in mental healthcare. Using NLP algorithms that can be trained on mental health diseases and guidelines to provide cognitive behavioral therapy (CBT) for patients suffering from depression PTSD, and anxiety. Virtual therapists are also being used to engage with autistic patients to improve their communication skills. Woebot is one such chatbot that provides CBT for patients.

Recommender engines work by recognizing similarities and extrapolating choices

Retail

Whether we know it or not, we’ve all experienced NLP at work in retail, in the form of chatbots.

Chatbots have become a basic tool in customer service, thanks to their ability to:

Answer questions

Schedule meetings

Process orders

Track fulfilment

Make recommendations

Onboard new users

Chatbots can reduce dependency on human interaction by a significant margin by automating common queries.

In addition to chatbots, NLP-based virtual assistants too offer many advantages, Such virtual assistants can take the form of a mobile application that customers can use to communicate with the store through text or voice. In-store bots can suggest products, locate a desired product, provide information on promotions, etc.

Market research

Retailers depend heavily on market intelligence to guide their planning and strategy. Conventional methods that rely on human facilitation are vulnerable to bias. NLP eliminates that and is also faster and more accurate. Techniques like web-scraping can help retailers extract data (from blogs, social media interactions, news websites, etc.) as well as product data. This can be combined with NLP to detect market trends and consumer sentiment analysis to help guide decision-making.

Semantic search

We’ve all used the search widget to find what we want while surfing online shops. Many e-tailers are using NLP to power semantic searches to understand the shopper’s requirement and put it into context. This enables retailers to tailor recommendations to match the searcher’s intent, leading to higher sales, and fewer abandoned carts.

Fintech

The business of banking and insurance involves a lot of documentation. From ascertaining creditworthiness to weeding out fraud to generating reports there is a lot of tedious, time-consuming work that goes into the typical workday. NLP offers a way out of the tedium, raising both efficiency and accuracy.

(For an in-depth understanding of the various ways NLP is revolutionizing Fintech, read: How NLP is changing the Fintech world.)

Credit rating

Scoring credit of an individual or an organization is a statistical analysis that lenders must perform to determine creditworthiness before any further loan can be sanctioned.

NLP can play a big role in credit scoring by extracting specific information from unstructured documents like loan documentation, income/expenditure statements, investments, etc., supplemented with information gleaned from personal profiles, such as social media accounts. This information can be fed through an ML algorithm into credit scoring software to determine creditworthiness.

Claims management

NLP—often used in conjunction with OCR—can be used to sift through unstructured and structured text using commonly used terminology to detect needed information to process claims.

Reporting

NLP plus ML can be used to comb through unstructured financial statements and related documents. Such as invoices, payment receipts, etc. The extracted information goes into an RPA bot, or any automated solution, to generate financial reports in pre-customized formats.

Auditing

NLP simplifies and eases the task of financial auditing by automating screening of financial documentation, classifying financial statements, and highlighting similarities and differences in documentation…all of which help detect deviations in financial statements.

Fraud is rampant in Fintech industries, but NLP, in combination with ML and predictive analytics can be used to detect fraud in many ways. For instance, by detecting deceptive communication (use of elevated or reduced frequency of certain words, tone of voice, etc.) and identifying the association between these features and fraudulent financial statements.

With recruitment processes increasingly being conducted digitally, there is a lot of scope for NLP to make a difference. Some fundamental use cases include:

Evaluation of CVs

ML algorithms that have been trained on certain keywords, can be used to extract relevant information and classify candidates based on the similarity between their profiles and the job descriptions of the positions. NLP can also summarize resumes that match desired profiles, allowing recruiters to go through resumes faster.

Virtual hiring assistant

Just like a virtual assistant can be used to perform certain actions using NLP. Virtual hiring assistants, or recruitment chatbots, as they are known, can speed up the recruitment process in the following ways:

Screening resumes

Scheduling interviews

Answer candidates’ questions online

Create candidate profiles—and summaries of resumes

NLP is particularly efficient on interview platforms that allow video recording, uploading of documents such as reference letters and certificates. NLP can detect specific words that reflect desirable or undesirable traits, transcribe video content, and summarize it.

Employee satisfaction

NLP can be used to analyze employees’ happiness on the job, as well as identify friction areas, difficulties, bias, et al. NLP can be used to screen feedback, employee comments on social media, employee websites, etc. This helps HR pre-empt potential conflict, identify training needs, keep employees engaged, and generally optimize work culture.

Cybersecurity

Spam

NLP models trained on text classification can be used to filter out spam emails by detecting words/phrases/sentences associated with spam.

Steps followed by NLP models to detect spam

Data exfiltration

Unauthorized movement of data is an insidious security breach; it includes copying or transferring of restricted data from one device to another. Hackers typically use techniques like domain name system tunneling (DNS queries for information sent from a user’s computer to a DNS server) or phishing emails to gather personal information. NLP can successfully identify such DNS queries, malicious language, and spot text anomalies to detect malware and prevent data exfiltration.

There are many ways NLP can help your business benefit, from enhancing efficiencies to cutting costs. To understand how you can leverage NLP in your organization, contact AAIC today.

How NLP is Changing the Fintech World

Tue, 18 Oct 2022 07:46:28 +0000

The term ‘moonshot’, in technology, refers to an ambitious, groundbreaking project; one undertaken without assurance of short-term profitability, but which, if it works out, can change the world in a definitive way. The term probably owes its origin to the idiom “shooting for the moon”— implying an outside chance of success. AI has always held out the potential for moonshots. And in 2013, the MD Anderson Cancer Center launched its own moon shot: a project that aimed to diagnose certain forms of cancer using IBM’s Watson cognitive system, and recommend treatment. In 2017, after exceeding costs of US$62, the project was put on hold. While this is a sadly familiar ending with most moonshots, the technology itself had a lot of other, less ambitious, uses to offer. In contrast with the long shot of moonshots, there are several lower hanging fruit that AI/Ml technologies offer, and one that has seen the greatest use in recent times is a subsection of AI called: Natural Language Processing (NLP). And the fintech industry has been quick to see its benefits and put it to use.

NLP use-cases In fintech

Credit scoring

One of the most basic uses of NLP is credit scoring. This is a statistical analysis that lenders, banks, and insurers routinely conduct, prior to sanctioning a loan, to ascertain the creditworthiness of an individual or a business. The process was, traditionally, a manual one involving poring through multiple documents, such as income, investments, expenses, etc., and extracting data that can be used to analyze creditworthiness.

NLP streamlines, simplifies, and speeds up the process by extracting relevant data from unstructured documents and feeding it to credit scoring software to determine the credit score. That’s not all. NLP can also be used to extract supplementary information from personal profiles , such as social media accounts, mobile applications, etc.

Managing insurance claims

NLP can be used in combination with Optical Character Recognition (OCR) software to analyze insurance claims. This approach can be used to comb through structured and unstructured text data in order to detect the right information to process insurance claims. The application then feeds the information to an ML algorithm, which labels the data according to the specific sections of the claim application form, with the terminology that is commonly filled into it.

Financial auditing

Financial auditing processes can be simplified and speeded up using NLP to automate certain parts, for instance:

Screening of financial documents

Classifying financial statement content

Identifying similarities and differences in documents

This, also enables flagging of anomalies in financial statements.

Fraud detection

NLP plus ML and predictive analytics can be easily used to detect fraud and misinterpreted information from unstructured financial documents.

In a study, researchers successfully used NLP linguistic models to identify deceptive emails—identified by a certain frequency or reduced frequency of first-person pronouns and specific words, words connoting negative emotions, and action verbs. Using a classifier algorithm, researchers were able to analyze linguistic features of annual reports, including voice, active versus passive tone, and readability, and detect any association between these features and fraudulent financial statements.

Loan & mortgage fraud detection

Loan and mortgage applications see a lot of criminal activity, such as the use of a stolen or fake identity to apply for a loan. Fraudsters establish credit history by using the identity to set up a number of credit lines, and finally max out all the credit lines and disappear. Such applications take a long time to detect and even longer to deny. Using a logistical regression model—easily explainable to regulators—with features like loan transaction details, bank statements, credit score, history on banking activities and credit history, bank account details, borrower’s identity, age, income and asset information, and other public information, the AI-powered model was able to flag fraudulent applications as good or greater accuracy than humans

AI-powered NLP technology can also be used to make data-driven decisions that give businesses a competitive edge. By searching real-time private data stored on platforms like Evernote, OneDrive, and SharePoint, businesses can acquire new customers.

Another business area where NLP solutions can make a huge difference is customer service. One UK high street bank saw this in action when they integrated an AI-powered automatic email responder. The solutions automated common queries, sending them to the right department without manual intervention. This enabled the bank to speed up replies with 95% accuracy. And with more and more training data generated with each interaction the scope for fine-tuning the model is infinite.

Email assistants are also able to conduct sentiment analysis on data, using keywords, phrases, and combinations to identify such aspects as emotion and tone of voice. These findings are helping businesses modify their own interactions, resulting in a much higher level of customer satisfaction

Voice assistants and chatbots

AI/ML-powered chatbots and voice assistants can deliver near-sentient experiences, easing the pressure on human staff, while improving customer experiences.

Conclusion

In addition to the above use cases, there are many, many other areas where AI/ML technologies like NLP can raise competitiveness, improve bottom lines and enhance experiences, not just for business stakeholders and their customers but also for their staff.

NLP in particular can be leveraged on interview platforms to analyze candidate sentiment, screen uploaded documentations, check for references, detect specific keywords that reflect positive or negative behavior, and more. NLP is also particularly useful in such areas as detecting employee sentiment, for e.g. detect job satisfaction, racial/sexual bias, etc. from feedback surveys, social media posts and comments, etc. this can help HR pre-emptively address conflict areas, build trainings, engage employees, and create the optimal work culture.

NLP – How it works and how it benefits you

Wed, 12 Oct 2022 07:33:10 +0000

Every time you ask Alexa (or Siri or Cortana, or Google…) to perform a task and it responds in a manner that is almost human, we take it for granted. But there’s a lot that goes on under the hood, and this article takes a closer look at the AI technology that makes human-machine interactions possible: NLP. (Natural Language Processing)

More to AI than Meets the Eye

Human speech is complicated. Not only do we speak in diverse languages like English, Spanish, French, etc. even those who speak a single language natively borrow words and phrases from other languages, almost unconsciously. In addition, there are numerous dialects, and each has its own set of grammar and syntax rules, slang, etc. In addition, we mumble, stammer, and use filler words, like, well, ‘like’. And writing is just as complex with misspelled words, abbreviations, and incorrect or omitted punctuation. Computers on the other hand speak “Machine” a language comprising only zeroes and ones in countless different patterns, bridging the two has only been made possible recently through the availability of big data—and the computing muscle to run complex algorithms to convert communications into zeroes and ones, and produce requested actions.

When a person asks his voice assistant (Siri/Alexa/etc.) to perform an action, they may do so in myriad ways, using different words, phrases, slang, sentence fragments, etc. But the application that runs the voice assistant must understand what is requested by breaking up the language into its basic parts, understand the pieces, and the context that gives it meaning, and respond appropriately…all in the space of a second or two.

How NLP relates to AI

Natural Language Processing is new, but the idea isn’t. The benefits of being able to communicate with machines were always apparent. More than 70 years ago, programmers used punch cards (cards with holes corresponding to zeroes and ones) to interact with computers. It was a tedious and manual process that few understood and fewer could perform. Then in 1952, Bell Labs created the first speech recognition, which could identify all 10 digits. It was called Audrey, but it was slow and thus quickly abandoned.

In 1971, the US DoD agency DARPA (Defense Advanced Research Projects Agency) developed a system, named Harpy, that could recognize more than a thousand words. This was a precursor of real-time speech recognition.

Over the past 50 years, thanks to new technologies, and computing power, coupled with the existence of—and the ability—to handle massive data sets, we have gained the ability to communicate with voice assistants like Siri and Alexa without skipping a beat.

Importance of NLP

Handling large volumes of text

NLP has made it possible for computers to read text, hear speech, break it down into its smallest component parts, understand it, interpret it, and respond. And computers can do it without fatigue or error. This is of huge benefit to many fields of human endeavor, where staggering volumes of unstructured data are generated—from medical records to social media. Automation will enable organizations to go through this data, analyze text, extract the required information, and do so quickly and accurately.

How NLP Works

Step 1. Sentence Segmentation: Breaking down text/speech into its elemental component parts.

Step 2. This is called tokenization: If you’ve ever diagrammed a sentence in school, you’ve done the same job manually. The objective is the same: to understand the component parts, their relationships to each other, and how they work together to create meaning.

Step 3. Stemming & Lemmatization: Stemming simple chops the ends off words to get to the root. Unfortunately, this doesn’t always work, as randomly chopping off the ends sometimes leaves a word fragment that has no meaning. Lemmatization groups words with similar inflextions to get to the root word.

Step 4. Filtering out the noise: We use a number of words that appear frequently and which can be safely filtered out without affecting the statistical analysis.

E.g. words like ‘a’, ‘the’, ‘and’, ‘this, ‘is’…

Step 5. Dependency parsing: This is done to understand how the words in relate to each other

Step 6. Assigning POS (Parts of Speech) Tags: Here each word is linked to its grammatical function to understand context. This is important when you consider how we often use nouns (even proper nouns) as verbs, for instance, “I need to google that.”

Step 7. Named Entity Recognition: In this step, the application detects a named entity, for instance, a person’s name, place, location, organization, etc.

Step 8: Chunking: The opposite of segmentation, chunking involves grouping individual pieces of information to form bigger fragments.

Phases of NLP

These stages can also be interpreted as the following phases

Lexical Analysis: This is the first phase, wherein the source content is scanned and the whole text is divided into paragraphs, sentences, words, and as individual characters

Syntactical Analysis: This is also known as parsing, and is done to understand the relationship between the words and phrases. E.g. Delhi came to John. Clearly, this sentence is incorrect, and is thus rejected by the Syntactic Analyzer

Semantic Analysis: This phase focuses on the literal meaning of the words and phrases

Discourse Integration: In this phase the sentences that precede a sentence and the ones that follow are taken into account to better understand the content

Pragmatic Analysis: This is the final NLP phase. It is done to understand the intended effect through context. For instance: Please open the door, is interpreted as a request, not an order.

NLP Applications

There are many, many general applications for NLP, such as translation, autocomplete, automated speech recognition, and conversational AI/Chatbots. But beyond interacting with Siri or Alexa, NLP offers many practical applications, here are some of the most common.

Spam filtering. If you’ve ever looked through your spam folder and studied the subject lines, you’ll notice a lot of similarities. This is Bayesian spam filtering at work. It is a statistical NLP technique that identifies junk from words in a spam list

Search. Websites that provide a search bar to enable visitors to search for a specific topic are using NLP methods, viz. topic modeling, entity extraction, and/or content categorization.

Transcripts: Popular video channels offer automatic (text) transcripts of the audio, this is NLP at work.

Social media analytics. NLP is routinely used to track awareness and gauge sentiment about topics.

Sentiment analysis

NLP for Fintech

Insurance and financial services organizations generate a vast amount of data—both structured and unstructured—NLP can help them extract relevant information in situations like long term contracts (with many annexes). NLP is also highly beneficial in risk assessment, enabling the organization to extract relevant data, via NLP processes such as entity recognition as well as historical data, credit and account histories, et al, and estimate a candidate’s loan risk. NLP algorithms can save time and improve accuracy in fraud detection, too, by automating some of the processes involved in reviewing loan documentation. Document classification is another area where fintech can benefit from NLP. Algorithms that can identify content associated with a particular type of document and classify it accordingly, will save auditors hours of manual labor. Fintech industries are also prime candidates for NLP use cases like sentiment analysis—to gauge trust sentiments, by extracting relevant information from social media posts, opinions, etc.

NLP in Healthcare

There are several use cases of NLP in healthcare, including clinical documentation, clinical decision support, clinical trial matching, and virtual transcribing, among others

NLP Benefits

NLP tools have never been so accessible: They help business process massive volumes of text data, streamline operations, improve efficiencies, enhance customer satisfaction, and reduce costs.

Text analysis at scale: Data that would take weeks of manual analysis can be processed in minutes or seconds

No bias, no errors: Humans are prone to mistakes and their inherent bias can skew results, NLP tools—once they’ve been trained and running—have neither. And they are indefatigable.

Streamlined processes and cost savings. NLP allows you to scale, 24×7. It also requires you to employ minimal staff. And it frees them from repetitive tasks and the potential for manual error.

Improved customer satisfaction: Connecting NLP tools to your data systems, for example, can analyze your customer feedback in real-time, so if needed you can contact them right away. NLP also helps you understand your customer base, improve market segmentation, and enhance customer lifetime value.

Happier employees: Relieving employees of the tedium of repetitive tasks, allows them to focus on doing their jobs better. It removes fatigue and improves motivation.

Actionable insights: With NLP you can easily break down data related to matters such as online surveys, reviews, etc. This lets you do away with guesswork or cursory analysis, and discover real, actionable insights that can bring real results.

Book a meeting with our Experts

Create Email Salutation Identification ML model and Run it in AWS Lambda

Tue, 05 Oct 2021 12:58:15 +0000

Why?

There are many tools out there which need to feed the email body to some ML model for some analysis or for further processing. But to them the first line which is in 40% of cases a salutation or a greeting line is a noise.

The model we are creating will help identify and remove the salutation line from the actual email body.
Running the model in Lambda helps keep our AWS bill in control.

Let’s get started…!

We will input an email body to the model and it should return True if the first line of the email body is greeting else it should return False

Dataset

For 7Targets, we wanted to create a model that would remove the salutation line for further processing of emails. To create an ML model you need a dataset on which the model is trained. For our case, we needed a set of emails as a dataset. In our case, we used the emails we have. We manually classified the emails and marked them if they contained greetings.

Preprocessing

To create feature vectors for the email, we first had to preprocess the data. Then removed the empty lines from every email and then selected the first line for further processing. For the sake of simplicity, we will always check if the first line of the email is a salutation or not.

Feature Vector Generation

Now from the first line of the email that I got from the above step, we had to generate the feature vectors which are then fed to the model for training. We then wrote the code to convert the first line of email into the 3 features described as below:

Salutation Score: We first created a list of commonly used salutations in the email such as dear, hello, hi, respected, good morning, etc. Then, found out the Levenshtein similarity between the first line and all of the above salutations. Finally, gave the score to the sentence as maximum of all the similarity.
Uppercase Score: Salutations generally starts with uppercase letter. For example, Dear Tanmay, Respected Sir, etc. So we gave the sentence uppercase score according to the total number of letters starting with uppercase alphabet.
Sentence Length Score: Usually salutations are shorter in length than most of the other sentences in the email. They are mostly 2-4 words in the whole sentence. We gave the sentence length score accordingly. Longer sentence gets less score and shorter sentence gets more score.

Model Training

We first split the data into sets of training and testing data with a ratio of 3:1 and chose Random Forest Classifier as the classifier for the project. Trained the model with training data and then tested the model and got accuracy to be 97%. The accuracy was higher than I had expected. Finally, we saved the model into a pickle file using joblib for later use.

How did we use the model?

We had to run real time inference of the data on AWS Lambda, because we use serverless technology a lot. We first created the AWS Lambda Layer from the scikit learn library and used the AWS prebuilt Numpy layer. Wrote the inference code and pushed the pickle file of the model into the Lambda. It worked like a charm and we are now using it in production.

Summing up!

In this article, I have demonstrated the simplest workflow required to develop a machine-learning model used to identify salutations in email.

Though, there are a few more improvements that would be required in this model. Like, if there is no newline between the salutation and the email body then the model would not work well. Which we will discuss at a later time.

Hit me up on my email if you have any questions.

Custom Software Development | Applied AI Consulting

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