Tag: Int.

Targeting for Success: Customer Segmentation and Retention Strategies for BFS Companies

Targeting for Success: Customer Segmentation and Retention Strategies for BFS Companies

May 26, 2023

The importance of proper customer retention and customer segmentation is unparalleled in the banking and financial services sector. Retention is crucial since it is always more beneficial to retain more customers who not only add to the BFS company’s revenues and recommend it to others, but are also not as costly to retain in comparison to the acquisition of newer customers. Satisfied and long-term customers are not only more amenable towards price or other fluctuations, but are also more likely to engage in word of mouth recommendations. With proper segmentation, BFS firms can target customers better, depending on their specific needs. Customer retention strategies for BFS companies Here are a few customer retention strategies that BFS firms can use: Customer segmentation and how it is essential for BFS firms Customer segmentation is crucial for banking and financial services firms. Before venturing into data analytics, you should undertake segmentation on the basis of behavioral patterns. This will be influenced by things like preferences for rewards/loyal programs/promotions/deals and also buying patterns, overall frequencies for purchases, and other parameters. This will help you roll out targeted marketing and recommendation campaigns for various segments/groups based on these insights. Customer segmentation involves tailoring your content for ensuring the delivery of more relevant and useful marketing campaigns for specific customer groups in place of choosing generic messaging for everyone. Here are some segmentation strategies that you should follow: Segment-wise communication and engagement strategies, along with tailoring messaging for every segment will help you create better personas of your targeted customers. You can then leverage insights to come up with the best marketing campaigns tailored to customer requirements. FAQs 1. How can BFS companies effectively identify and define their target customer segments? BFS firms can more effectively identify their targeted customer segments and define them better through proper segmentation. They can do this on the basis of data analysis, helping them classify customers on the basis of parameters such as age group, buying habits, patterns, products/services most required, life stage or life cycle in the customer journey, and so on. 2. What are the key benefits of customer segmentation for BFS companies? Customer segmentation helps BFS companies in several ways, enabling them to target specific groups better with tailored marketing campaigns, recommendations, and products and services. BFS firms can know which target groups require specific solutions and offer the same accordingly. 3. What challenges or obstacles do BFS companies face when implementing customer segmentation and retention strategies? BFS companies face a few obstacles in the implementation of customer retention and segmentation strategies. These include the absence of technological expertise, compatibility and integration issues along with the inability to leverage data analytics for enhancing customer experiences and satisfaction alike. 4. How can BFS companies measure the effectiveness and ROI of their customer segmentation and retention initiatives? BFS companies can track the ROI and effectiveness of customer retention and segmentation initiatives by undertaking data analytics relating to parameters such as sales growth across segments, changes in consumer patterns, customer feedback trends, churn rates, and so on.

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Harnessing the Benefits of Hashgraph-Association

Accelerating Business Growth: Harnessing the Benefits of Hashgraph-Association

May 24, 2023

Accelerating business growth with decentralised networks and consensus algorithms is possible with the Hashgraph association. Enterprises require solutions like Hashgraph association for overcoming hurdles in terms of transforming themselves towards developing more Web 3.0 based use cases and using institutional solutions. Some of these potential use cases include developing secure and flexible smart contracts, data privacy mechanisms, decentralised finance, real-time supply chain visibility, innovative loyalty programs, targeted and automated content creation, diverse usage of augmented reality, and a lot more. Blockchain-based transactions and assets are fast becoming more effective ways of doing business for enterprises and this is where they require effective solutions like Hedera Hashgraph. The Hashgraph association is a boon for enterprises in several ways, helping them find ways to explore solutions on Web 3.0. More and more enterprise use cases can be accelerated for development and deployment alike, throughout a proven and efficient framework on the Hedera network. Companies can now build for the future with decentralised and more advanced applications. Why Hashgraph Association? Hedera is one of the most innovative and enterprise-grade public ledgers for a completely decentralised economy. It is also a sustainable solution for enterprises. The Hashgraph Association is a non-profit and independent entity that focuses on developing a better ecosystem for enterprises, startups, and Government institutions worldwide, tapping Hedera Hashgraph capabilities for the design and development of decentralised applications and other solutions. The network at Hedera is developed by a global community on the network that is governed by a council of top industry players including Avery Dennison, Chainlink, Boeing, DLA Piper, Deutsche Telekom, DBS Bank, LG Electronics, Tata Communications, Shinhan Bank, Nomura Holdings, Wipro, and University College London, among many others. Here are some other points worth noting: The management team includes industry experts like Co-Founder & Co-CEO Mance Harmon who has previously served as the Head of Architecture and Labs at Pig Identity, while founding two technology start-ups. Co-CEO and Co-Founder Dr. Leemon Baird is the inventor of the Hashgraph distributed consensus algorithm and the co-founder at Hedera. He has more than 20 years of experience in the technology and startup space, while having been a Professor of Computer Science at the US Air Force Academy and founding multiple startups. A little more about the Hedera Hashgraph Association The Hashgraph association offers grant funding to start-ups, enterprises, and Government programs for developing and executing solutions powered by Hedera. At the same time, there are HBAR grants and other venture development programs for helping create projects with future potential through the Hedera network. The association also assumes the role of a co-investor in big-ticket projects, while empowering enterprises for competing in the digital assets and decentralised finance segment throughout various business areas. There is also an initiative to reach out to Government organisations and backing national blockchain-related initiatives for the promotion of the higher adoption of DLT solutions and contributions towards economic growth. The Hashgraph Innovation Program taps the worldwide network while collaborating with trusted partners. There are ideation and professional training workshops for building the right perspectives required for competing and adapting in the decentralised economy. Other initiatives include design-thinking and modelling proof-of-concepts based on several use cases as explored through the ideation workshops. The Hashgraph association also looks into the development of MVPs or minimum viable products along with integrating Hedera-backed solutions into enterprises. Hence, businesses can benefit greatly from the Hashgraph association with regard to turbocharging and accelerating future growth in a decentralised economy with robust solutions and support with easy implementation throughout the ecosystem.

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The concept of DAOs: Decentralized Autonomous Organizations

The concept of DAOs: Decentralised Autonomous Organisations

May 16, 2023

Decentralised autonomous organisations (DAOs) have been making waves lately, throughout the technology spectrum. DAOs are specific management structures leveraging blockchain technologies for automating a few aspects of transaction processing and voting. There is no central legal entity involved in decentralised autonomous organisations (DAOs) that holds responsibility for the regulation of the projects. There is smooth governance and transparency ensured through the decentralisation mechanism and distributed ledger technology. Smart contracts are made and tested for ensuring that vital details are not missed. Tokens are used as incentives for all validators in DAOs, ensuring their active, swift, and fair participation. DAOs have wide applications throughout the blockchain, including cryptocurrencies and Web 3.0 which is the proposed new-generation web architecture driven by decentralisation. The future blueprint here is that decentralised autonomous organisations or DAOs may enable easier creation of decentralised entities that respect stakeholder interests outside any one party’s control. They may be used for raise funds for specific purposes/projects while forming newer business mechanisms. They may also enable future automation of several financial steps and processes on blockchain platforms, making sure that stakeholders get compensation as per universally-agreed rules, while automating shared votes based on specific support, investment levels, or even engagement. How it could pan out for decentralised autonomous organisations? Many industry players feel that decentralised autonomous organisations or DAOs may eventually replace the inherent trust quotient in personal ties or with central authorities through specially-made blockchain smart contracts. DAOs may also automate a model where every stakeholder gets adequate compensation and a long-term project stake. Here are a few key points worth noting in this regard: However, there are a few disadvantages of decentralised autonomous organisations or DAOs. Automated smart contracts may be hard to tweak whenever any problem is found, while hackers may also find some loopholes for the illegal misappropriation of funds against stakeholder interests. Participants may also shell out higher transaction charges for every transaction, while human intervention will also be necessary for the implementation of legal and physical procedures. The legal applicability and status of DAOs is still being understood by global authorities, which mean current risks for all investors.  DAOs are still in their infancy. In the short term, they could have a major effect on overall fundraising for particular projects/objectives. Several services that promote causes will find them effective in the near future, enabling voting by participants alongside. DAOs may also enable newer investment entities while automating the procedure of returning money to all participants. DAOs may eventually help establish decentralised business models for support and investments alike. Participants may get tokens for their support and long-term stake in any venture. It is early days as of yet, although DAOs could well transform into some of the most exciting future trends. FAQs What are the benefits of a DAO? DAOs ensure automated procedural efficiency along with higher transparency and the absence of any central authority. It will enable swifter fundraising while enabling streamlined distribution of incentives/rewards/returns simultaneously. Are DAOs legal? The first U.S. State to legally recognise DAOs was Wyoming in 2021. They were recognised as limited liability companies, giving them a defined corporate structure and legal protection. Some other States are also considering the legalities of DAOs, while they are also attaining legal status in several other countries. What are the risks associated with participating in a DAO? DAOs are still works in progress, with several authorities yet to recognise them legally. This presents risks for participants, including personal liability. Any DAO participant may be liable for any action taken by an organisation, even when it was not personally authorised by him/her. What is the difference between a DAO and a traditional organisation? Any traditional organisation has its own centralised and legal entity. This is absent in a decentralised autonomous organisation (DAO).

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The Future of Contact Centres: How Chat GPT 4 is Revolutionising Customer Service in the BFS sector

The Future of Contact Centres: How Chat GPT 4 is Revolutionising Customer Service in the BFS sector

May 11, 2023

Customer service is a crucial function for most BFS (banking and financial services) entities, particularly since it directly influences customer satisfaction and engagement. There have been concerted efforts made by leading firms in this space towards ramping up service operations. What is the future technology for contact centres? Chat GPT 4 has come into the picture, presenting a compelling case for BFS players in terms of effectively handling customer service. Contact centres have a vital role to play in the contemporary business environment, enabling support and service to a diverse customer range.  Yet, the management and operations of contact centres can be difficult, taking the increase in customer inquiries into account, along with the need for efficiently and swiftly responding to queries. Chat GPT can completely revolutionise customer service, enabling greater benefits for BFS firms. Here’s how. A little on Chat GPT How is Chat GPT used in banking? Can Chat GPT be used for customer service? Before answering these questions, it is pertinent to note that Chat GPT is an acclaimed large language model that has been created by OpenAI. It is based on the GPT-4 and GPT-3.5 architectures, while being tailored to simulate conversations like human beings and enabling automated customer query responses. Chat GPT can also understand natural language, while being trained on diverse aspects, making it suitable for deployment across contact centres. How can Chat GPT contribute towards enhancing customer service and contact centre functions? Here are some of the ways in which Chat GPT can greatly enhance customer service and contact centre operations: It will also greatly boost customer experiences, while scaling up customer satisfaction levels considerably alongside. From recommendations to custom offers, Chat GPT can do this and more. It will help BFS players handle sudden increases in inquiries at peak times, making sure that customers get responses in a timely manner. Chat GPT for boosting the performance of contact centre employees Chat GPT can not only enhance customer service levels, but also enhance the overall performance of employees at contact centres. Here are some of the ways in which it can ensure the same: Chat GPT 4 can thus automate various tasks, thereby enhancing efficiency and overall productivity levels at contact centres. GPT-4 can completely transform communications with customers along with the total engagement levels alongside. Companies in the BFS space can automate their entire customer support functions and response/answer generation, while scaling up accuracy levels simultaneously. Businesses can take care of higher customer inquiry volumes without incurring extra costs. GPT 4 can also help BFS players build more engaging and customised customer experiences through a deeper understanding of their queries and tailoring responses accordingly. It can ensure superior customer communications through quicker content creation that saves time and resources greatly. It can generate content that is updated and accurate for customers, while keeping the relevance quotient high. Hence, it can be stated that Chat GPT 4 can be a propeller towards better functioning of contact centres in the banking and financial services segment, along with considerably ramping up customer service for better efficiency and productivity alike.  FAQs 1. How does Chat GPT 4 improve customer service in the BFS sector? Chat GPT 4 can greatly boost customer service in the banking and financial services space by automating customer responses with more personalisation and relevant answers. It can also handle a higher volume of inquiries without additional costs. 2. Can Chat GPT 4 handle complex customer inquiries and provide accurate solutions? Chat GPT 4 can easily tackle customer inquiries of a complex nature with deep-language learning and provide accurate and updated responses to queries that are increasingly relevant and not run-of-the-mill. 3. What measures are taken to ensure the security of customer data when using Chat GPT 4? Some of these measures include ensuring that there is user awareness about data usage, encryption and access control. Data regulations and privacy laws should also be adhered to by companies while using Chat GPT 4. 4. How does Chat GPT 4 reduce customer wait time and improve response times? Chat GPT 4 lowers waiting times for customers and boosts response times through generating quicker answers to customer queries along with higher accuracy levels. Customers can get answers via Chatbots and voice-bots without having to wait for human responses.

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The Power of Data: How Analytics is Revolutionising Debt Collection in BFS

May 10, 2023

Data, as always, is proving to be the real game-changer for BFS (banking and financial sector) companies. Analytics is driving higher efficiencies and benefits for BFS players, including debt collection. What is the role of analytics in BFSI debt collection? Here’s finding out. With regard to better engagement with consumers for debt collection, BFS players have always depended on conventional models including phone calls and emails/letters. The aim here is application in bulk, which has often led to several experiences that are not as pleasant for both parties (borrowers and lenders). Customers now expect increasing personalisation with diverse requirements and preferences. By tapping machine learning, AI, and predictive analytics, companies are now acting on data-based insights while resolving several problems across sectors, including debt collections. It has led to simpler, faster, and easier systems and procedures for consumers and banks alike, with advanced data science abilities providing teams cutting-edge tools that they require for higher transparency and standardising operational processes.  How is data analytics transforming debt collections for BFS entities? Here are some of the ways in which analytics is gradually revolutionising debt collections for banks and financial service providers. A. Building custom outreach strategies- Debt collection is not only about physically reaching out to borrowers. The blueprint has to be wider today, in sync with fast-changing banking ecosystems. Personalising the outreach to borrowers across types and personas, with stage-based plans across channels (deriving from anticipated consumer responses at each stage) is highly necessary for BFS entities today. Data and analytics is now spurring decision-making throughout the entire value chain of collections, along with saving time, energy, and costs for companies simultaneously.  B. Debt resolution strategies- Analytics has enabled better debt resolution blueprints on the basis of insights. These include the following aspects: Risk Classification of Borrowers- ML-based algorithms can accurately forecast the chances of delinquency of borrowers, deploying innumerable parameters as inputs. Due to resource limitations, calling every defaulter is next to impossible, not to mention the need to engage debtors, to avoid future non-payment risks and delinquencies. Hence, analytics is what enables hyper-personalisation in this case, enabling agents to communicate the right message in specific scenarios to particular groups of customers.  Channel Forecasts- On the basis of insights on earlier communication and behaviour of customers, BFS companies can predict the suitable template, channel, frequency, language, and time to contact borrowers. Several borrowers also prefer digital communication and their preferences may be better identified by algorithms. Optimising Blueprints- Tracking real-time borrower behaviour throughout multiple channels enables the creation of a personalised blueprint and mechanism for debt collection. Predicting Intent-to-Pay– It encompasses forecasting the willingness of borrowers to repay the money, based on outreach measures and historical measures for working out the priorities for the coming day in terms of tele-calling or other outreach initiatives.  Internal transaction information, when fused with other factors and behavioural triggers can predict delinquencies, while enabling BFS companies to accurately build pro-active strategies for customer outreach. For instance, lenders may swiftly gain a perspective of customers’ aggrieved/negative reactions or reducing account balance and their inter-relationships. Better Borrower Understanding- Using analytics for debt collections will enable an examination of transactional, demographic, and behavioural information.  This will help BFS players gain a better understanding of consumers, while identifying specific patterns and getting insights that help them develop strategic blueprints for collections.  Monitoring borrower responses to several types of messages is also helpful in determining ideal frequencies for interaction and suitable timelines. Risk-driven segmentation may also be useful for better targeting. Insight-Driven Debt Collection Decisions- Debt collection decisions can be taken in a better way, driven by analytics-based insights. Segmentation, predictive systems, deep behavioural analysis, and other blueprints are all possible for BFS companies. Insights are helpful for warning banking entities early regarding delinquencies and defaults in the future. It will help banks work out when people are likely to default and come up with custom strategies for mitigating these situations. These insights also help build suitable responses for interactions with borrowers. Issues with continual follow-up communication will be resolved with data-based intelligence. Banking entities will have suitable knowledge for catering to customers with suitable details at the right times.  Building Personalised Relationships- Customer relationships and experiences are crucial aspects for all BFS entities. Data-driven insights and analytics may help greatly in both these departments. Lenders can have language/region-based responses for customers. As can be seen, data analytics can completely transform debt collections for banks and financial services players. Data errors, incorrect entry, issues with detecting frauds have been identified and resolved with smart and new-age AI-based tools. At the same time, debt collection can be enhanced greatly through better insights and real-time visibility into the process. From reaching out to customers at the right time and with personalised offerings, to improving engagement and scaling up predictive/forecasting abilities, it enables BFS players to resolve long-standing problems and convert debt collection into a seamless and standardised process. FAQs 1. What is debt collection in BFS? Debt collection refers to the mechanism for collecting and recovering unpaid/due debt from borrowers.  2. What kind of data is collected and analysed for debt collection? The types of data collected and analysed for debt collection include historical customer data on engagement and interactions, behavioural data, preferences, demographic data, socio-economic and macro-economic data, past financial transaction history of customers, and more.  3. What are the benefits of using data analytics in debt collection? There are several benefits of using data analytics for debt collection, including customising communication and outreach with borrowers, predicting/forecasting future chances of defaults and non-payment, working out better debt collection strategies, and standardising the process.  4. Can data analytics help in predicting future defaulters? Data analytics can be immensely helpful in the prediction of future customer defaults, through generating insights on past purchasing behaviour, financial history, transaction history, and many other parameters. This helps banking and financial services companies forecast the likelihood of defaults and take pro-active steps accordingly.

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Hackathon Diaries #7 - The Third Eye

Hackathon Diaries #7 – The Third Eye

May 5, 2023

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.

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The impact of 3D printing on pharmaceutical manufacturing

The Impact Of 3D Printing On Pharmaceutical Manufacturing And The Potential For Personalised Dosage Forms And Drug Delivery Systems

April 28, 2023

3D printing has the potential to not only revolutionise core business segments like manufacturing and construction, but also the pharmaceutical industry. From pharmaceutical manufacturing to personalised dosage forms and drug delivery systems, there are several applications of this technology in the sector. It has led to a major shift with a change visible from conventional medicine mass production towards personalised drug products for each individual. The concept has future potential with regard to enabling advantages for the industry, patients, and pharmacists, through offering on-demand production and design of flexible medicine formulations, complete with personalised sizes, shapes, dosages, drug releases, and combinations of multiple drugs. At the same time, 3D printing may be integrated into applications like precision medicine and additive manufacturing with the technology enabling the creation of medicines personalised for therapeutic needs of patients, including drug combinations, dosage, and drug release profiles along with personal requirements in terms of the flavour, texture, shape, and size. 3D printing also ensures multiple advantages for not just the pharmaceutical industry and clinical practices, while also helping lower overall costs and speeding up development cycles alongside. Pharmaceutical 3D printing system types 3D printing has several avatars in terms of its usage in the pharmaceutical manufacturing space. These include the following:  Design- Pharmacists and companies can design and tailor formulations with software, choosing sizes, shapes, and types that cater to clinical or pre-clinical needs. The designed formulation will be transferred digitally to the chosen 3D printer.  Develop- Printlets can be created through the insertion of the necessary ink cartridge into the printer. The suitable parameters are chosen including temperature, resolution, and printing, which are usually based on the characteristics of the drug, type of printer, and the outcomes which are desired.  Dispense- The 3D printer can automatically enable the preparation of printed formulations on a layer-wise basis, which will be prepared for dispensing through the pharmacist.  Some methods of printing include SLS (selective laser sintering), FDM (fused deposition modelling), BJ (binder jet), DPE (direct powder extrusion), and SSE (semi-solid extrusion). Every type of technology has its own specific technical attributes while enabling the production of personalised drugs with diverse attributes. Key benefits of 3D Printing for pharmaceutical manufacturing Here are some of the biggest advantages of 3D printing in the pharmaceutical manufacturing space, right from drug delivery systems to personalised dosage forms.  Personalisation of treatments on the basis of individual or therapeutic needs of patients.  Patients can ultimately select formulation types from available catalogues, leading to preferential colours, textures, flavours, sizes, and shapes. This will mean higher autonomy of patients along with engagement across various pathways of treatments and higher adherence to medicines.  Medicines can be produced with exact dosages as needed by patients, or even flexible types of dosages.  The efficiency of treatment can be improved while also lowering the risks of any unwanted effects due to inaccurate and unnecessary dosages.  These abilities may be helpful for pediatric patients, who may not always prefer traditional formulations that are mass-produced.  3D printing in the pharmaceutical industry may benefit senior citizens or older people, especially those with complex dosage needs and higher tablet volumes regularly. Combinations of multiple dosages and drugs along with drug release profiles into single formulations may be advantageous for this section of the populace.  Clinical pharmacy practices may also benefit from easy integration of 3D printers, with SSE, FDM, and DPE being especially helpful in these cases. Pharmacists will be able to leverage flexible and automatic systems of compounding which may generate tailored forms of dosages upon demand, based on evolving situational or patient requirements. On-site printers will naturally enhance access to medicines for various categories of patients, lower manufacturing costs, and also hasten discharge timelines due to lower labour needs.  The overall application of this technology can reduce the total time required between drug discoveries and marketing formulations along with the overall costs linked to the same.  3D printing may be deployed as an alternative method of production by the industry for offering mass-personalised/customised medicines. Formulations may be customised for patients as mentioned for on-demand production throughout decentralised areas including clinics, pharmacies, and even the homes of patients.  Down the line, 3D printing may be a veritable game-changer for the pharmaceutical industry, enabling personalisation and formulations, along with efficient drug delivery as well, while simultaneously lowering go-to-market timelines, costs, and many other hassles involved in the process for pharmaceutical companies. FAQs What is 3D printing and how does it work in the pharmaceutical industry? 3D printing or additive manufacturing is the procedure of creating three-dimensional solid items from digital files. It works through the manufacturing of specific, multi-drug, and personalised formulations in the pharmaceutical industry.  How can 3D printing be used to create personalised dosage forms and drug delivery systems? 3D printing can be leveraged for the creation of personalised forms of dosages and drug delivery systems, through either multi-drug combinations or formulations, or on-demand dosages with differentiated flavors, textures, sizes, and shapes. How does 3D printing impact the speed and efficiency of pharmaceutical manufacturing?  3D printing, especially on-site, lowers the time taken by pharmaceutical manufacturing entities to produce on-demand drugs. It also reduces the time between discovery and marketing of formulations by a great extent. The entire procedure becomes more efficient with lower labour, costs, and higher efficiency with minimal logistics.  What are the regulatory considerations for 3D printed pharmaceuticals? The medical products made by 3D printers are regulated by the FDA. The regulatory review that is needed depends on the type of product that is being manufactured, along with its intended usage, and the potential risk factors for patients.

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The emergence of decentralised finance (DeFi) and the potential impact on traditional banking and financial services

The Emergence Of Decentralised Finance (DeFi) And The Potential Impact On Traditional Banking And Financial Services

April 24, 2023

Decentralised finance (DeFi) has made a huge splash worldwide, with its potential for traditional banking disruption. It is a rapidly growing financial technology that uses secured and distributed ledgers, based on blockchain and smart contracts, just like cryptocurrencies. But how is it contributing towards disruption or financial services innovation? In the U.S., for instance, the SEC (Securities and Exchange Commission) and Federal Reserve have clearly outlined the regulations for centralised financial entities such as brokerages and banks that customers depend on accessing financial services and capital directly. DeFi poses a challenge to this centralised financial setup through empowering people with peer-to-peer digital transfers/exchanges. It also contributes towards the elimination of fees charged by financial institutions and banks for the usage of specific services. Those holding their money in secure digital wallets can instantly transfer funds, while anyone with internet can make use of DeFi. What is centralised finance? For understanding decentralised finance, one should first have an idea of what centralised finance is all about. Here are some core points worth noting in this regard:  Centralised finance has the money held by the banks and third parties. They are the ones who enable money transfers across multiple parties, each of them charging fees for the usage of services.  Networks clear charges and request payments from banks. Every chain entity gets payments for services provided.  All transactions are supervised in this system, right from local banking services to applying for loans. How does decentralised finance (DeFi) work? Decentralised finance (DeFi) has huge potential for traditional banking disruption. Here are some points worth noting:  This system does away with intermediaries by enabling merchants, individuals, and businesses to take care of financial transactions with new and emerging technologies.  DeFi makes use of security protocols, software, connectivity, and hardware advancements via peer-to-peer financial networks.  Individuals can easily trade, lend, or borrow funds whenever they get an internet connection, using software for verifying and recording financial transactions throughout financial databases that are distributed.  Distributed databases are those which are readily accessible throughout multiple locations, gathering and aggregating data across users and leveraging a consensus-based mechanism for verification.  Decentralised finance (DeFi) does away with the need for any centralised financing model, through enabling any individual to make use of financial services almost anywhere, irrespective of their identity or location. DeFi applications enable higher control over funds for users via personal wallets and trading solutions catering to individuals.  Decentralised finance does not ensure complete anonymity. Transactions, while not having individual names, can be traced throughout all entities with access, including the law and Governments.  DeFi makes use of blockchain technology as used by cryptocurrencies. The blockchain is the secured and distributed ledger/database. Transactions get recorded through blocks and verified by users. Once verifiers agree to transactions, the blocks are closed and then encrypted. Another block is made with data on the earlier block within the same. The blocks are conjoined through data in every proceeding block, which gives it the blockchain moniker. Information in earlier blocks cannot be modified without any effect on the following ones. Hence, there is no way to change a blockchain.  How DeFi is being used in the financial sector Decentralised finance (DeFi) is being used widely in the financial sector, with the following being the major take-aways:   P2P (peer-to-peer) financial transactions, right from payments through applications and issuing loans.  DeFi is enabling direct interest rate negotiation between two parties and lending through its networks, equating to lower fees.  Anyone with internet can access DeFi platforms and there are no locational limitations on transactions.  Smart contracts on blockchain and records of competed transactions can be easily reviewed and are immutable.  Income-generation and capital transfer abilities for investors with high security. Here’s how it is disrupting traditional protocols: DeFi is enabling lending/borrowing at scale between unknown parties and minus intermediaries with automatic setting of interest rates, based on demand and supply. Loans are secured through over-collateralisation, with loan access anywhere and anytime.  DeFi is also enabling the de-centralised trading and development of derivatives for various assets like commodities, stocks, and even currencies. Decentralised asset management for cryptocurrency is another growing trend.  Decentralised exchange concepts have come up, with cryptocurrency holders no longer needing to leave the arena for token swapping. DEX has several smart contracts with reserves of liquidity, operating as per pre-defined mechanisms of pricing.  Decentralised insurance is also available, covering bugs for smart contracts in this entirely new space. This is a major risk area for DeFi users, and is covered by these plans.  As can be seen, crypto-based decentralised finance has already reached an advanced stage in terms of its evolution. It is steadily taking care of all the necessary functions of a financial system as a result. DeFi could well be the next big thing in global finance, provided it can navigate security threats successfully.  FAQs What is decentralised finance (DeFi) and how does it differ from traditional finance? Decentralised finance is where distributed and secured ledgers are used with blockchain technology like cryptocurrencies. It means peer-to-peer transfers without higher fees for transactions as charged by all the entities in a traditional transaction chain. It can enable anytime and anywhere transactions between unknown parties, with automatic conditions and smart contracts, eliminating intermediaries.  How does DeFi work and what are the key components of DeFi platforms?  The main components include specific hardware, software, stablecoins, and so on. The infrastructure is continually evolving, and the system works through an independent yet secured and highly traceable network on the blockchain. Transactions are recorded, stored, and are verifiable easily. At the same time, there are no intermediaries and resultant charges. Parties can directly engage in transactions with automatic setting of interest rates or other crucial parameters.  What are the potential advantages of DeFi over traditional finance?  DeFi is a transparent and open system as compared to the closed and centralised system followed by traditional financial institutions. Transactions are public and may be viewed by any individual. They are readily traceable as well. At

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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)

April 6, 2023

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. 

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Addressing drug shortages with advanced analytics

Addressing drug shortages with advanced analytics

April 3, 2023

Drug shortages have become a part and parcel of modern healthcare systems due to several reasons. While there is a sizeable economic impact of drug shortages for manufacturers and pharmacies alike, there are widespread community and social disadvantages as well. Pharmacies or clinics running out of medicine stocks are representatives of a scenario that is often witnessed worldwide and with frightening consequences.  For example, Europe is already seeing shortages of commonly-used medicines. A survey by the Pharmaceutical Group of the European Union (EU) had 100% of 29 member nations reporting shortages of medicines amongst community pharmacists. 76% also stated how shortages had worsened than the earlier year (the survey was implemented between 14th November and 31st December 2022). The UK is also witnessing HRT shortages according to reports, while hospitals in the U.S. are also reporting issues with procurement for liquid ibuprofen, while ADHD diagnoses have gone up in the U.S. as well, leading to shortages of vital drugs for the same. Mexico is witnessing chronic shortages and unfulfilled prescriptions and supply fluctuations and disruptions have been seen widely throughout Asia too.  What are the reasons for medicine shortages?  Wondering about the reason for drug shortages? There are quite a few that can be noted in this context:  Higher seasonal illness outbreaks in the aftermath of COVID-19, leading to skyrocketing average annual demand for medicines that is higher than normal in several categories.  The inability of pharmaceutical companies to meet such unprecedented demand, with excess capacity restricted for cost control.  Global supply chain impact along with higher energy costs and inflation have impacted global drug manufacturers who have to contend with pricing measures.  Stockpiling by customers due to sudden drug shortages.  Over-prescribing by the system.  Reports estimate that the National Health Service in the UK loses a whopping 300 million pounds annually owing to partially-used or unused medication which cannot be reused or recycled.  Lack of systems for forecasting and identifying supply shortages, while ensuring proper inventory management.  Drug Shortage Solutions That May Work  There are a few drug shortage solutions that may be effective for combating and reducing shortages.  Data and analytics are enabling better access towards medicines worldwide while enabling superior supply and demand management for individual patients and pharmacies alike.  Real-time pharmacy, hospital, and clinical data will enable a proper understanding of the demand for specific drugs/medical products.  Leveraging electronic and public health records for enabling healthcare stakeholders to report demand figures for drugs, without revealing confidential patient data.  Opportunities for better inventory and supply chain management with AI (artificial intelligence) and machine learning (ML).  Generic entities may leverage smarter technologies for lowering manufacturing costs by up to 20% while enhancing production. Smarter and connected factories with proper insights and data analysis can enable higher savings and reliable deliveries.  Companies may look at higher procurement of local active ingredients while depending on go-to nations for the same. Boosting supply and production levels, along with harnessing real-time data analytics will enable tackling this scenario.  Supervised machine learning and analytics models can help in forecasting/predicting shortages for most drugs used throughout various categories, price points, and age groups.  Modelling can enable healthcare stakeholders to understand more about the issues behind drug shortages while analytics can also help predict demand for specific drugs based on historical data and current trends.  Pharmacies and other players may not have access to data on the supply side, although they have demand-side information. They will be able to gain more visibility into the supply chains of manufacturers with an integrated information-sharing system.  Data analytics-driven insights for optimizing orders and eventually lowering the effect of drug shortages on pharmaceutical and healthcare operations.  Systems for tracking and reporting drug shortages, including aspects like the frequency, drugs involved, period, causes, duration, managing strategies, impacts, and future shortages too.  Real-time identification and tracking of patients receiving shorter supplies of drugs by hospitals, clinics, and pharmacies. Immediate patient identification regulations for capturing present drug utilization across multiple categories.  Real-time identification and addressing situations along with finding out drugs in shorter supply. Predictive abilities enable higher time for researching material for alternative agents or making suitable arrangements for drug acquisition from other sites or facilities.  Once supply levels normalize for a drug, pharmacists and healthcare stakeholders may discontinue their surveillance regulations without waiting for technical assistance. Real-time data-filtering and reporting abilities are leveraged for viewing drug usage trends and prescription patterns throughout healthcare systems. These insights may enable higher standardization of drug management across institutions, while also facilitating better training of clinicians for lowering care variations.  Advanced data analytics will help address drug shortages and enable better inventory management simultaneously. However, suitable implementation, technological integration, and awareness are necessary for the same.  FAQs How can advanced analytics be used to address drug shortages? Advanced analytics can be deployed for tackling drug shortages through real-time tracking and surveillance of prescription trends and drug demand, forecasting shortages, and enabling better drug supply management.  What are the benefits of using advanced analytics to address drug shortages? Advanced analytics goes a long way towards helping tackle drug shortages, enabling forecasting future demand and shortages, identifying patterns for better management, and also enabling better global medicine access.  What are the challenges of using advanced analytics to address drug shortages? Challenges include technological integration, legacy systems integration, awareness regarding best practices, quality data generation, and more.  What are the best practices for implementing advanced analytics for drug shortage management? Best practices include unified and integrated public databases, suitable data modelling systems, suitable protocols for data security and privacy, and swift reporting mechanisms for demand and shortages.

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