Category: AI & MI

The Future of Retail: Predictive Analytics Revolution

The Future of Retail: Predictive Analytics Revolution

Predictive analytics or retail analytics are completely transforming the rules of the game as far as business growth and expansion are concerned. Retail predictive analytics is the process of gathering data on retail processes and operations, while deploying the same for enhancing customer insights for businesses. This also enables better decision-making about marketing campaigns and product/service offerings while learning how to improve the business better. Here’s taking a brief look at the same. How predictive analytics is revolutionising the retail industry Predictive analytics in retail is already being used to gather historical data to make predictions, answer questions of businesses and improve operations. This may include demand forecasting and better inventory management and tracking. This also enables more insights into customer behavior and ensuring higher satisfaction, thereby equating to greater customer retention simultaneously.  Here are some key points that you should keep in mind: The benefits of using predictive analytics in retail : The future of predictive analytics in retail : FAQs 1.How does predictive analytics enhance customer personalisation and shopping experiences? Predictive analytics enables better personalisation and shopping experiences through insights generated from historical data. This pertains to shopper habits, preferences, buying patterns and other metrics. 2.What types of data are used in predictive analytics for retail? There are several kinds of data used in predictive analytics for retail. These include data gathered from multiple channels like stores, direct customer feedback, interactions, websites, apps and more. 3.What are the emerging trends and technologies driving the predictive analytics revolution in retail? The emerging technologies and trends driving the predictive analytics revolution in retail include on-shelf analytics, location analytics, shopper-level analytics and transaction-level analytics. AI and machine learning are also being used for automating data gathering and insight collection along with customer interactions. 4.How does predictive analytics help retailers in demand forecasting and inventory management? Predictive analytics gives retailers more visibility into seasonal and historical patterns in terms of consumer demand, sales and particular product requirements and performance. Hence, they can better forecast future demand and manage their inventory accordingly in order to minimise losses.

Read More »
How Data Analytics is Reshaping the Life Sciences Landscape

From Insights to Innovations: How Data Analytics is Reshaping the Life Sciences Landscape

Data analytics is completely transforming the life sciences industry in recent years, having a profound impact on its operational aspects, just like it has revolutionised healthcare in recent years. Big data is positively impacting everything from supply chain and logistics to drug discovery, thereby proving to be a shot in the arm for life sciences companies.  What is the future of data in life sciences? How data analytics is transforming? Data analytics has completely transformed the life sciences industry in recent years. When it comes to drug discovery, one of the key components of the sector, not even 10% of drug candidates make it to the market after clinical trials. The lower rate of success in this regard can be attributed to various factors. Machine learning is also enabling pattern detection through structured and unstructured data. This is being pieced together by data analytics, gathering information across electronic recordings, laboratory results, demographic data, IoT data, medical journals, clinical notes (using natural language processing) and more. Big data is being deployed to identify distribution, causation, patterns, and determinants throughout higher volumes of complementary and differing data points for more information about present diseases. It will enhance the overall accuracy and speed of treatment and diagnosis, with huge data volumes collected from multiple sources. This will help personalise diagnosis, treatment, monitoring, planning and drug discovery. Data analytics naturally has a huge role to play in this regard.  What are some key examples of how data analytics has led to innovations in the life sciences field? FAQs 1.What are the future prospects and trends for data analytics in the life sciences industry? Data analytics will play a vital role in the life sciences industry in the future, enabling personalisation of medicines, helping identify new drug candidates, enabling better real-world evidence analysis and improving supply chain management. 2.What types of data are utilised in life sciences data analytics? There are several types of data utilised by the life science industry for analytics including data from wearables, clinical records, trials, diagnostics, medical imaging, medical devices and more sources. 3.What challenges does the life sciences industry face in implementing data analytics? Some of the challenges in implementing data analytics include poor quality of data, silos, lack of interoperability and also issues in managing huge volumes of data. 4. How can data analytics help in the identification of patterns and trends for disease prevention and epidemiology? Data analytics can help analyse epidemiological data through several methods. It can help summarise, infer, organise, describe and gather data. This will naturally help identify various trends and patterns pertaining to prevention of diseases, distribution, risk factors, and treatments.

Read More »
Improving Supply Chain Management Using analytics, ML, AI

Improving Supply Chain Management Using analytics, ML, AI

Supply chain management is fast being transformed with technologies like supply chain analytics, AI, and machine learning in supply chain. A study by McKinsey has found how implementing artificial intelligence in supply chain management has enable major improvements. Those adopting such technologies have already seen logistics costs going down by 15%, along with a reduction of 35% in their inventory levels, in tandem with service levels going up by 65%. Hence, the future potential of AI, ML, and analytics driven supply chain management is massive, to say the least. How AI and analytics helps with better supply chain management Here are some ways in which analytics enhances supply chain management greatly. These include the following: This covers the implementation of ERP and CRM systems along with SRM software and business intelligence solutions. Performance can be analysed at a broader level while analytics also helps predict and minimize future risks and any negative effects on the channels for distribution. ML is also used for identifying key factors in supply chain and other logistic data with constraint-based modelling and algorithms. This helps employees take better decision on stocking, while offering insights to enhance inventory management. AI does make a difference in tandem with ML and analytics, helping companies save costs and enhance revenues through identifying better procurement and shipping rates, pinpointing supply chain profit procedure changes, and managing transportation contracts. A centralized database will offer visibility into every aspect of the entire supply chain for enabling better decision-making. These technologies are also enabling the identification of vital partners and suppliers, enabling companies to standardize options at lower costs while predicting all performance indicators based on compliance factors. Most big enterprises will switch to smart robots for warehouse operations in the next few years, as per industry expectations. There will also be a need for more specialized and integrated SCM-oriented AI and other tools. Analytics will drive decision-making throughout every stage of the supply chain management process, while non-implementation of the same will only lead to companies missing the bus in terms of revenue growth, cost reductions, better tracking, and higher efficiency across the board. FAQs AI is enabling easier tracking and real-time insights for companies, along with enabling better inventory management, demand forecasts, stocking, sourcing, and several other benefits. Some of the main considerations include features like real-time visibility into every aspect of the supply chain management process, integration with existing systems, easy accessibility, user-friendly interface, powerful analytics and forecasting features, and more. Some of the key challenges include poor data quality or an unorganized data gathering process, along with issues relating to technological integration and employee understanding. Companies can easily leverage data analytics for identifying future risks and combating them accordingly. They can do this by predicting/forecasting future demand, user needs, patterns, and market fluctuations.

Read More »
Navigating Risk in Digital Lending

Navigating Risk in Digital Lending

Digital lending risks abound in the current scenario, which require careful navigation. The need for suitable risk management in lending is increasing by the day. Here is a snapshot of the biggest online lending risks and how financial institutions can navigate the same. 1. Higher consumer risks in digital lending– Banks are facing higher operational risks in online lending. They are streamlining the process through the adoption of paperless loan approvals, while using automation to enhance overall quality and time alike. There is a concern regarding risks of customer data safety, since they share account and personal details, credit history and a lot more on applications. Banks and financial institutions are now looking at increasing cyber security in lending and measures to implement mechanisms for data privacy in lending. Dedicated security prevention is possible with the right technological framework and solutions which BFS players are steadily opting for.  2. Credit risks– There is a need for proper credit risk assessment in digital lending, considering how customers with poor or low credit may lead to growing NPAs and hassles for BFS players At the same time, there is always a risk of defaults in the future. Hence, these companies need to use data analytics and advanced credit evaluation systems for ascertaining the creditworthiness of borrowers. There is also a need for swift assessment and proper credit evaluation models can help address the same digitally. 3. Compliance and regulatory risks- BFS players have to increasingly factor in compliance risks in fintech lending. The RBI and other authorities are coming up with evolving guidelines and regulatory mechanisms that have to be adhered to in a strict manner without lapses. Usage of artificial intelligence, insights, and regulatory mechanisms is the solution to navigate these challenges. 4. Market risks in digital lending– There are always risks of market changes, volatility, and fluctuations that may turn out to be problematic for digital lenders. Hence, using advanced AI-based forecasting models and analytics could help gauge market patterns, trends, and consumer preferences. This will aid the creation of products and services, while boosting strategic decision-making simultaneously. 5.Operational risks- Digital lending often works through a transaction process that has multiple layers. Many services are often outsourced to several entities. It sometimes becomes complicated, in terms of redressing grievances, taking care of customer complaints, and ensure effective and prompt service. This can be navigated with the use of advanced AI-based Chatbots for resolving most customer queries and prompt engagement with customers. From algorithms that personalise customer journeys and recommend products to those that quickly resolve issues or take care of complaints, automation can work wonders in this case. 6.Fraud risks– There are always risks pertaining to fraudulent applications, transactions, and breach of trust within the ecosystem. BFS players can ensure better fraud prevention in digital lending with advanced automation. Historical data analytics can help gauge patterns of a fraudulent nature. This can help detect and combat frauds in a better manner. NBFCs and banks should endeavour to periodically get security audits done, while implementing robust Cyber security measures at the same time. From firewalls and advanced encryption protocols to multi-factor authentication, there are several options available. Identity verification can be scaled up with biometric authentication, digital KYC, and quick credit bureau checks. Data analytics can be used to analyse credit histories and verify income and debt-to-income ratios. Diversifying lending portfolios is also recommended for combating market risks more effectively. FAQs 1.What are the different risk associated with lending? There are several risks associated with lending. These include data privacy, operational, credit, regulatory, market and fraud risks. 2.What are the risks associated with digital finance? Digital finance faces risks linked to the privacy of consumer data, breaches in security frameworks, preventing frauds, credit risks, and compliance/regulatory risks. 3.How do market and economic factors impact digital lending risks? Fluctuations in the market on account of geopolitical and economic factors can impact consumer behaviour and also lending rates. The effect on these variables may lead to business risks for the institution. 4.What measures can be taken to address the risk of borrower default in digital lending? Borrower default risks can be minimised with proper data analytics for evaluating historical borrower data, credit histories, debt-to-income ratios, track records, and other vital information. This helps establish the creditworthiness of individual buyers, while flagging risky customers in advance.

Read More »
INT. Pulse

INT. PULSE

Dear Colleague, each month, all of us at INT. Marketing dive into a dizzying research gig to write the best opening section of this newsletter (Fyi, Pulse has 35K+ monthly subscribers now 😎). Here’s this month’s winner – the Forrest Gump of tech, aka, Yahoo! And Why Is That? Sample this – Yahoo had a peak dotcom-days valuation of USD125 billion but ultimately – hold our coffees – was sold to Verison for USD4.8 billion in 2016. Here are five 🤯 Yahoo moments: In 1998 Yahoo refused to buy Google for USD1 million. 4 years later, in 2002, Google said it would sell to Yahoo for USD5 billion (but Yahoo only offered USD3 billion, meaning – no deal, sir.) ⏩ to 2006, Yahoo offered USD1 billion for Facebook but Zuckerberg turned it down. Sources said, if Yahoo had increased their bid to USD1.1 billion, Facebook’s board may have pushed for sale, but Yahoo didn’t budge. Come 2008, Microsoft offered to buy Yahoo for USD46 billion, but Yahoo said ‘Noooooo Wayyyyy!’ And finally, in 2013, Yahoo bought Tumblr for USD1.1 billion, writing it down to USD230 million just 3 years later. Psst: Also, instead of Tumblr, it considered buying Netflix for USD4 billion, now worth USD140 billion. STATS: Fastest Finger Hand First In a world where acronyms like DAU and MAU rule the roost, your mother-in-law will tell you that it would be wise to know the number of years it took each of the following to gain 50 million users, per the World of Statistics: Airlines: 68 yearsCars: 62 yearsTelephones: 50 yearsCredit Cards: 28 yearsTV: 22 yearsComputers: 14 yearsThe Internet: 7 yearsPayPal: 5 yearsYouTube: 4 yearsFacebook: 3 yearsTwitter: 2 yearsWeChat: 1 year ChatGPT: A little less than 30 days, and……🏆 PornHub: 19 days AI/ML: How Big Tech Effed Up (Major Time) ­All of us are in the know about tech layoffs, triggered by the arrival of generative AI. However, while dishing out pink slips may have made investors happy, there is another side to the story. Yeah? And What Is That? The AI Trap. Let us explain. As generative AI and coding took off, massive layoffs, led by big tech firms were triggered across the tech world. But, but, but, all these former employees are now going and building serious competition in 1/10th the time it would take biggies to get there. On the other hand, the big guys are perpetually stuck in meeting/webinar hell, arguing over use cases, tech stack, safety, and deployment methods, while solo developers knock the wind out of them, meaning, the long tail of software just grew 100X. Was It Avoidable? Probably not. You see, Covid tailwinds resulted in a huge surplus as people spent more time online and the big boys used that tailwind to hire, expecting never-ending growth. As the Covid winds died down, growth in tech crashed, leaving big tech players bloated, less agile, and ready to walk into the AI trap, with arms wide open. 💡 At INT., we have an agile AI and Advanced Analytics setup that is doing some cool work in the BFSI, Life Sciences and Retail space. Reach out to Dipak Singh to know how you can reduce costs and improve customer acquisition. ☕️ The coffee is on us! BFSI: Fintech Market Correction Is ‘Short Term’ For the last year or so, fintech exuberance has been served a super-strong shot of black coffee, with regulations clamping down hard, valuations dropping by 60% across the sector, and funding drying up by almost 43%, YoY. So, Is Fintech Dying? In one word – NO WAY! Per this BCG-QED report, the fintech growth story is only in its initial stage and is expected to grow to a USD1.5 trillion industry by 2030. Here are some key takeaways from that report. Sit back and get a hold of this. Where Does Fintech Stand Today? Word on the street is that the fintech journey is still at infancy and will continue to disrupt the financial services industry over time. Basis of that belief is; customer experience remains poor and with over 50% of the global population remaining unbanked or underbanked, financial technology (FinTech) is the only means to unlock new use cases, resulting in growth going up by leaps and bounds. Deepak Goyal, MD, BCG, opines that all stakeholders must therefore seize the moment. Regulators need to be proactive and lead from the front. Incumbents should partner with fintechs to accelerate their own digital journeys. APAC To Lead The Fintech Show Asia-Pacific is this big unserviced market, with almost USD4 trillion in financial services revenue pools, and is slated to outpace the US to become the world’s top fintech market by 2030. This growth will be driven primarily by Emerging APAC (e.g. China, India, and Indonesia) at a projected CAGR of 27%. 🔥 What’s Hot & Happening In Fintech? While payments led the last leg, B2B2X and B2b (serving small businesses) will lead the next. B2B2X is made up of B2B2C (enabling other players to better serve consumers), B2B2B (enabling other players to better serve businesses), and financial infrastructure players. The B2B2X market is expected to grow at a 25% CAGR to reach USD440 billion in annual revenues by 2030. 💡 Need to create and implement your B2B2X strategy? Souvik Chaki is your go to person, so feel free. Stuff We Are Watching ­📌 Are Credit Cards Dying? Because from now on, you can get easy credit on UPI as well. Here’s how this disruptive feature can boost the Indian Economy, or turn into a recovery nightmare, depending on who’s reading. 📌 Big Tech Work Cultures: Sample this and guess which company these people hail from – Super thoughtful, similar to Microsoft, platform mindset, but sometimes too slow to act. All Big Tech work cultures, summed up by one observer here… 📌 Why Optimise Code Anymore: Remember the old times when most software installation was done via 1.4 MB floppy disks? With storage space restrictions dead, why should developers optimise code?

Read More »

Insurtech Revolutionises Insurance with Personalised, Faster, and Affordable Solutions

Insurtech is the latest phenomenon that is revolutionising insurance across the spectrum. The insurance industry is innovating with the use of technology with an aim towards making products, services and solutions more affordable, personalised and quicker for customers. Here are some of the digital technology offerings that are playing a major role in this space: AI (Artificial Intelligence)- This is one of the biggest innovations contributing towards automating the processing of claims, enabling better detection of frauds and also enhancing customer service. AI enables more accurate and improved pricing and assessments of risks. It helps insurance companies manage risks better while lowering costs simultaneously. It also ensures that customers get more personalised and cost-effective insurance offerings. 2. IoT- The Internet of Things is another aspect which enables cost reduction and personalisation alike. It also boosts customer experiences greatly. The insurance industry is leveraging IoT devices for collecting information on consumer behaviour and environments, including home security, driving habits, health, and so on. This is facilitating accurate assessments of risks and pricing, while helping develop new products tailored to customer needs. For example, IoT devices may be used to develop insurance products where customers are charged on actual driving distance and usage. 3. Blockchain– This digital technology functions through distributed ledgers, enabling transparent and secure transactions without centralised intermediaries. It is being used in insurtech for streamlining the processing of claims and lowering frauds along with enhancing overall data security too. 4. Mobile Apps- Insurtech also functions through new-age mobile apps for boosting customer experience and making claims processing simpler. Customers are getting more personalised recommendations and higher control over their policies. Mobile apps are also being used for tracking the status of claims, managing policy data, and getting personalised advice on products based on their behaviour and specific requirements. 5. Telematics- It is already being used for gathering data on customer driving behaviour and performance, enabling more accurate assessments of risks along with better pricing strategies. Products are thus tailored to meet the needs of customers in a more personalised manner. Why insurtech is gaining ground in the insurance industry These are some of the chief reasons behind the rising popularity of insurtech solutions throughout the mainstream insurance sector. FAQs 1. Can Insurtech solutions replace traditional insurance providers? Insurtech solutions can be replacements for conventional insurance offerings. However, they will not replace traditional providers completely. Rather, these companies will work closely with insurtech players to come up with better products and services for their customers. 2. Are Insurtech solutions regulated? The insurance industry is one of the highest-regulated sectors in the world. Insurtech is also similarly regulated since it is used by insurance companies for carrying out many of their functions. 3. How does Insurtech impact the insurance industry? Insurtech positively impacts the insurance industry by helping it reduce costs, automating manual and repetitive tasks, personalising customer experiences, scaling up overall efficiency, and making products/services more affordable for customers. Customers get more control over their journey with the insurance company and wait times are reduced considerably as well. 4. How can Insurtech solutions improve claims processing? Insurtech solutions can automate claims processing, thereby saving time and money for the company. They can gather data and verify the same minutely in quick time, while also eliminating frauds alongside. This leads to more accurate processing of claims without any risks of losses/fraud.

Read More »
Hackathon Diaries #7 - The Third Eye

Hackathon Diaries #7 – The Third Eye

Greetings, fellow coders and tech enthusiasts. It’s the 7th and final edition of the INT. Hackathon Diaries V1.0. But don’t shed a tear just yet, we will be back soon with the next edition as our in-house masterminds are up and running with new and innovative ideas all the time. We’ve saved one of the bests for last, and it’s a project sure to keep you wide awake: The Third Eye – Driver’s Drowsiness and Mobile Distraction Detection Solution. The Third Eye We all know how dangerous driving is when we’re tired or distracted. It’s like playing Russian roulette with our lives, and those of everyone else on the road. But fear not, as The Third Eye team has come up with a solution that’s so clever, it’ll make you wonder why nobody thought of it before. Using the latest computer vision and machine learning technology, The Third Eye system monitors drivers in real time, watching for telltale signs of drowsiness or distraction. It’s like having a personal wake-up call or a stern aunt, reminding you to keep your eyes on the road and your hands on the wheel. It is to create the leeway to a sustainable and protected world while driving. The Team Arijit Datta Arnab Kanti Ghosh Pabitra Bhunia Nitesh Kumar Singh Rahul Lohar Suva Samanta Explosive Growth in the Market  Per recent reports, the global market for drowsiness monitoring systems was valued at a staggering $2.2 billion in 2019 and is projected to grow to $3.3 billion by 2027 with a CAGR of 5.2% during the forecast period.  But that’s not all, folks. The global market for distracted driving prevention technology is expected to explode from $1.27 billion in 2019 to a whopping $2.9 billion by 2025, with a CAGR of 12.7% during the forecast period.  These numbers speak volumes about the urgent need for cutting-edge solutions that keep drivers alert and focused behind the wheel. So get ready to join the race to the top as we explore the latest developments in driver safety technology that are taking the market by storm. Resolution Realms Scope 1 – Drowsiness Detection Prepare the dataset: Collect and prepare data for drowsiness detection. Augment the data: Improve the model’s performance by data augmentation techniques. Split the dataset: Divide the prepared dataset into training and testing sets. Configure the model: Customise the YOLOv5 model for drowsiness detection by modifying configuration files to specify hyperparameters, input image size, and a number of classes. Train the model: Train the YOLOv5 model using the prepared training set and the configured model. Evaluation: Measure the model’s performance on the testing set using evaluation metrics such as precision, recall, and F1 score. Fine-tune: Adjust the hyperparameters and retrain the model on the entire dataset or a subset of it to fine-tune the model. Deployment: Integrate the trained model into a mobile or web application for real-world drowsiness detection. Scope 2 – Mobile Phone Distraction Data collection: Gather a dataset of images depicting instances of mobile phone distraction. Data Preparation: Transform the annotations into a format that is compatible with YOLOv5, such as COCO or YOLO. Model configuration: Set up the YOLOv5 model to recognize mobile phone distractions. Model training: Employ a deep learning framework like TensorFlow or PyTorch to train the YOLOv5 model on the training set. Evaluation: Test the trained model on the test set to determine its accuracy and performance. Deployment: Deploy the trained model onto our device. Alert mode: Once our device detects a driver using a mobile phone while driving, it will emit a continuous alert message until the driver puts down the phone. Tech Stack AI/ML Azure Map Service Smart Band Edge Computing  IoT Wow Factors Scene 1: Driver wearing sunglasses Infrared (IR) cameras detect the heat signatures of objects, including human eyes, even when they are partially obstructed by sunglasses. Scene 2: Driving at night Night Vision Camera tracks the driver’s face and eye movements. Scene 3: Presence of multiple faces before the camera Detection of only the front face. Conclusion Stay tuned and keep your eyes peeled for the next edition, where we’ll bring you more cutting-edge solutions and innovations that are driving the industry forward. See you there.

Read More »
Digital Asset Management - SharePoint Syntex to the Rescue

Hackathon Diaries #6-Digital Asset Management

The Hackathon Diaries are back, and they’re better than ever. Are you ready for an exhilarating ride? The 6th edition of Hackathon Diaries is here, and we’re taking on a challenge that’s sure to get your heart racing: digital asset management using Sharepoint Syntex. With its advanced capabilities, Syntex is transforming the way businesses manage their valuable digital assets. But the journey to mastering this technology won’t be easy. We’ll need to put our skills to the test and unleash our creativity to solve complex problems. So, get ready to witness innovation in action as we dive deep into this exciting new project. Digital Asset Management Digital assets are a critical component of any modern business, but managing them can be a daunting task. That’s where SharePoint Syntex comes in – an AI-powered engine that can transform the way organizations manage their digital assets. With Syntex, you can create a powerful Digital Asset Library system without any coding efforts, making it easy for your team to store, access, and analyse your most valuable information. By capturing the information in your business documents and transforming that information into working knowledge, Syntex enables your organisation to make quick data analyses and insights. It can extract key data points, classify documents, and even automate workflows with its advanced capabilities – all with just a few clicks. So why wait? Start unlocking the power of your digital assets today with Syntex and take your business to the next level. The Techie Meet the mastermind behind the magic – Aniruddho Kodali, the developer who brought this project to life. Problem Statement In today’s fast-paced business world, data is king. But with the sheer volume of information available, finding what you need can feel like searching for a needle in a haystack. The average worker spends a staggering 20% of their time searching for information, leading to lost productivity and missed opportunities.  But what if there was a solution that could cut that time by as much as 35%? Imagine a world where knowledge was easily searchable, accessible, and organized. That’s the challenge we’re taking on with our latest project: digital asset management using Sharepoint Syntex. We believe that with the right tools, managing overwhelming amounts of data can be a breeze. And with Sharepoint Syntex, we’re taking that belief to the next level. Our goal is to create a system that makes it easy for employees to find the information they need when they require it. Business Solution Syntex Content AI – Digital Asset Management In today’s fast-paced business world, information is king. But with the sheer volume of content available, managing it all can feel like an impossible task. That’s where Syntex Content AI for Digital Asset Management comes in – an innovative solution that transforms how content is created, processed, and discovered. By utilising the latest advancements in cloud and AI technology, Syntex empowers people and automates workflows at scale. It automatically reads, tags, and indexes high volumes of content, making it easy to find and connect information where it’s needed – in search, in applications, and as reusable knowledge. But Syntex is more than just a search engine. It manages your content throughout its lifecycle, providing robust analytics, security, and automated retention. And with features like auto classification, zero-touch information management, and reporting and visualisation, It modernises the way businesses approach information management and governance. Impacts Are you tired of your business spending countless hours and resources managing overwhelming amounts of content? Syntex Content AI is here to revolutionise the way you approach digital asset management – and save you money in the process. With Syntex’s advanced content classification and curation capabilities, businesses can save between $1.2 million to $3.3 million, reducing the need for costly professional services and streamlining content management. But that’s not all – Syntex’s improved discovery capabilities can save your business between $42 million to $127 million by making it easier to find and connect the information you need, when you need it. And with reduced reliance on legacy tools and professional services, businesses can save between $864,482 to $1.2 million – freeing up resources for other critical projects. With Syntex Content AI, businesses can unlock the power of their content and save money in the process. Don’t let inefficient content management hold you back – it’s time to discover the new possibilities of the future. 

Read More »
Hackathon Diaries #5 - Fraud Detection in Discharge Summary

Hackathon Diaries #5 – Fraud Detection in Discharge Summary

It’s time for the 5th edition of Hackathon Diaries. This time around, we are thrilled to present a project that is both timely and crucial – Fraud Detection in Discharge Summary. With healthcare fraud on the rise, there is a pressing need for innovative solutions that can identify and prevent fraudulent activities in the healthcare sector.  Our team of talented developers and data scientists have come together to tackle this challenge head-on and develop a system that can effectively detect fraudulent activities in discharge summaries. So, fasten your seatbelts and get ready for an exciting journey as we take you through our journey of building this game-changing solution. Fraud Detection in Discharge Summary Are you ready for a game-changer in the world of healthcare fraud detection? Our team of talented developers has come up with an innovative solution that will leave fraudsters shaking in their boots – the Fraud Detection in Discharge Summary project. By analysing a patient’s discharge summary, our system can quickly identify any suspicious or fraudulent activity and flag it for further investigation.  Say goodbye to financial losses due to fraudulent healthcare practices – our cutting-edge technology will help ensure that healthcare remains transparent and trustworthy for all.  The Team (Data Wizards) Arghya Chakraborty Anirban Bhattacharya Arindam Mukherjee Sourav Mukherjee Saurav Mandal Problem Statement Calling all healthcare warriors. Are you ready to take on one of the biggest problems facing insurance companies today? Providers falsifying or exaggerating hospital discharge summaries to receive higher reimbursement rates from insurers is costing them a fortune – and compromising the integrity of medical records. Besides financial Losses for insurance companies, the overall procedure consumes a good amount of time and also demands extensive levels of manual interventions. Proposed Solution The proposed solution is based on cutting-edge AI technology and is divided into two phases, each designed to accurately identify fraudulent activity and notify the relevant stakeholders. In Phase 1, the team developed an AI model that analyses contextual data and structural patterns within discharge summaries to identify any signs of fraud. This model is designed to identify inconsistencies and irregularities that may indicate fraudulent activity, and immediately notify the necessary parties to take action. Phase 1.1 focuses on the development phase, where the AI model will be trained to recognise and prevent fraud in handwritten discharge summaries. The implementation of AI-based content prevention techniques will be done to identify and flag any fraudulent activity. In Phase 2 (our future scope), the solution was taken to the next level by identifying discrepancies between discharge summaries and medical billings. The AI model is further trained on historic data to identify inconsistencies between provided discharge summaries and ideal discharge summaries. This would help identify fraudulent medical billing practices and notify the relevant stakeholders, helping to prevent further financial losses due to healthcare fraud. With this innovative solution, insurance organisations can rest assured that fraudulent activity in healthcare will be quickly and accurately identified to take appropriate actions to prevent further losses. Tech Stack Front-end: Django/Flask, HTML, CSS, and JS Back-end: Core Python, OCR, OpenCV, Database The Workflow How We Stand Out The solution is a first-of-its-kind that collects different fraud detection models under one umbrella, making it easier and more efficient. It boasts a blazing-fast processing time, taking an average of just 2.5 seconds per analysis. That means you can quickly identify and prevent fraudulent activity in real-time, without having to wait hours or days for results. Don’t settle for outdated and inefficient fraud detection methods – upgrade to our innovative solution today and take control of the fight against healthcare fraud.

Read More »
How the Large Language Models like GPT are revolutionising the AI space in all domains (BFSI, Pharma, and HealthCare)

How the Large Language Models like GPT are revolutionising the AI space in all domains (BFSI, Pharma, and HealthCare)

Large language models or LLMs are ushering in a widespread AI revolution throughout multiple business and industry domains. DALL-E-2 set the cat amongst the pigeons in the AI segment in July 2022, developed by OpenAI, before ChatGPT came into the picture. This has put the spotlight firmly on the invaluable role increasingly played by LLMs (large language models) across diverse sectors. Here’s examining the phenomenon in greater detail.  LLMs make a sizeable impact worldwide With natural language processing, machine learning, deep learning, and predictive analytics among other advanced tools, LLM neural networks are steadily widening the scope of impact of AI across the BFSI (banking, financial services, and insurance), pharma, healthcare, robotics, and gaming sectors among others.  Large language models are learning-based algorithms which can identify, summarise, predict, translate, and generate languages with the help of massive text-based datasets with negligible supervision and training. They are also taking care of varied tasks including answering queries, identifying and generating images, sounds, and text with accuracy, and also taking care of things like text-to-text, text-to-video, text-to-3D, and digital biology. LLMs are highly flexible while being able to successfully provide deep domain queries along with translating languages, understanding and summarising documents, writing text, and also computing various programs as per experts.  ChatGPT heralded a major shift in LLM usage since it works as a foundation of transformer neural networks and generative AI. It is now disrupting several enterprise applications simultaneously. These models are now combining scalable and easy architectures with AI hardware, customisable systems, frameworks, and automation with AI-based specialised infrastructure, making it possible to deploy and scale up the usage of LLMs throughout several mainstream enterprise and commercial applications via private and public clouds, and also through APIs.  How LLMs are disrupting sectors like healthcare, pharma, BFSI, and more Large language models are increasingly being hailed as massive disruptors throughout multiple sectors. Here are some aspects worth noting in this regard:  Pharma and Life Sciences:  Healthcare:  The impact of ChatGPT and other tools in healthcare becomes even more important when you consider how close to 1/3rd of adults in the U.S. alone, looking for medical advice online for self-diagnosis, with just 50% of them subsequently taking advice from physicians.  BFS:  Insurance:  The future should witness higher LLM adoption throughout varied business sectors. AI will be a never-ending blank canvas on which businesses will function more efficiently and smartly towards future growth and customer satisfaction alike. The practical value and potential of LLMs go far beyond image and text generation. They can be major new-gen disruptors in almost every space.  FAQs What are large language models? Large language models or LLMs are specialised language frameworks that have neural networks with multiple parameters that are trained on vast amounts of unlabelled text with the usage of self-supervised learning.  How are they limited and what are the challenges they encounter? LLMs have to be contextual and relevant to various industries, which necessitates better training. Personal data security risks, inconsistencies in accuracy, limited levels of controllability, and lack of proper training data are limitations and challenges that need to be overcome.  How cost-effective are the Large Language Models? While building an LLM does require sizeable costs, the end-savings for the organisation are considerable, right from saving costs on human resources and functions to automating diverse tasks.  What are some potential ethical concerns surrounding the use of large language models in various industries? Some concerns include data privacy, security, consent management, and so on. At the same time, there are concerns regarding these models replicating several stereotypes and biases since they are trained using vast datasets. This may lead to discriminatory or inaccurate results at times in their language. 

Read More »
MENU
CONTACT US

Let’s connect!

Loading form…

CONTACT US

Let’s connect!

    Privacy Policy.

    Almost there!

    Download the report

      Privacy Policy.