Category: AI & MI

Breaking Boundaries: When Blockchain Meets Artificial Intelligence

Both artificial intelligence and blockchain are arguably the biggest game-changers in the cutting-edge technology space today. And what happens when they both combine? Something on the lines of next-generation synergy is created. This magical blockchain-AI integration has several possibilities for path-breaking development in the future. Here are a few aspects that deserve your attention in this context.  What is the impact of blockchain and artificial intelligence? To gauge the impact of blockchain-AI integration would be a tough task, since as mentioned, it offers near-infinite possibilities for the world to leverage. Yet, a few things can be clearly identified in this regard:  AI and blockchain also have the potential to deliver great results for businesses in several sectors, including the following:   AI for Supply Chain Management The next-generation synergy achieved through the combination of AI and blockchain will generate immense value for businesses and stakeholders throughout industries. From optimised supply chains to better productivity throughout industries like life sciences, healthcare, and financial services, the advantages are innumerable, to say the least. Blockchain for Smart Contracts Here are a few points that you may consider in this regard:  For instance, IBM and Sonoco are collaborating to fix issues in the transportation of life-saving medication through enhancing the transparency of the supply chain. Pharma Portal is a dedicated platform that is powered by IBM Blockchain Transparent Supply. This monitors pharmaceuticals that are temperature-controlled across the supply chain for enabling reliable, trusted, and accurate data throughout several parties. In another example, Home Depot makes use of smart contracts on the blockchain for swift dispute resolution with its vendors. AI is also playing a crucial role in enabling superior supply chain management. FAQs 1.How can blockchain enhance the transparency, security, and trustworthiness of AI-powered systems and applications? Blockchain uses distributed ledger technology and is based on principles like consensus, decentralisation, and cryptography. This ensures higher transaction security, trust, and transparency. AI governance can easily verify, record, and audit data and decisions within this spectrum.  2. What are some real-world use cases where the convergence of blockchain and AI has led to significant advancements? There are several use cases in the real world where AI and blockchain have combined for multiple benefits. For instance, Home Depot is already using blockchain smart contracts for resolving disputes with its vendors. AI is also being leveraged for verification and insights in this case.  3. What are the potential challenges and obstacles in implementing blockchain and AI together, and how can they be overcome? Some of the major challenges include the need for more bandwidth and specialised technological/hardware capabilities. Others include integration with existing systems, technological expertise, data quality, and privacy guidelines.  4. How does the convergence of blockchain and AI foster innovation and drive new opportunities for startups and businesses? The fusion of AI and blockchain in innovative ways automatically help businesses and start-ups seize new opportunities. This enables the creation of highly efficient, secure, and transparent data management and exchange frameworks. Intelligent and automated decision-making systems can be leveraged for reliable and accurate results/outputs, triggering particular real-world outcomes. Data will always be tamper-proof and immutable while AI-based insights and automation will ensure higher productivity and lower costs at almost all levels. For instance, blockchain will ensure that you have tamper-proof and accurate data, which AI can analyse to unearth invaluable insights for businesses. 

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The Power of Retail Analytics in Elevating Customer Experience

The power of retail analytics in elevating customer experience

Behavioral analysis, retail trends and customer experience could well seem quasi-scientific terms that are increasingly used throughout the industry. But what will pleasantly surprise you is the fact that they are commonplace for retailers today.  What is powering things like conversion rate optimisation and insights driven by customer feedback? AI-powered analytics of course! Retail analytics is no longer restricted to only a subset of use cases for these technologies, but has come into the mainstream. It has spawned an entire universe of its own, redefining the operations and strategies of both digital and brick-and-motor retailers in recent years. It is not just instincts alone that shape successful retail operations today, but also data-driven insights and decision-making. Let us know more about the power of retail analytics especially in terms of boosting customer experiences. What is the power of retail analytics? To understand the power of retail analytics and how it has become a catalyst for change in the industry, here are some core points that deserve to be noted: How does analytics improve customer experience?  What are the future trends in retail analytics? Retail analytics will thus power not only future innovations in terms of better experiences and personalisation for customers, but also optimisation at scale. This will include inventory, logistics, supply chain, deliveries, operations, marketing, and advertising. Brands will depend more on analytic and other technological tools to revamp their core propositions in the coming years. It can safely be said that exciting times are afoot in space. FAQs 1.What are some advanced analytics techniques used in retail analytics? There are many advanced techniques of analytics that can be used within the spectrum of retail analytics. These include descriptive analytics, predictive analytics, and prescriptive analytics. Diagnostic analytics may also be used in some cases. 2. Provide us some of the best practices for using retail analytics. Some of the best practices include ensuring the quality of data across points along with suitable data gathering in compliance with regulatory guidelines. At the same time, customer data and forecasting should be done on a real-time basis with complete visibility and tracking. 3. How does retail analytics enable retailers to deliver personalised experiences to customers? Retailers can use retail analytics to unearth valuable insights on what customers desire at specific times of the year, what they browse for, and their previous purchase history. They will also know about the products selling well in particular locations and at particular times of the year. Individual customer engagement can also be tracked for a specific duration. All this data can be analysed to help improve customer experiences with personalisation recommendations, offers, and promotions. 4.What are some of the challenges of using retail analytics? Technological expertise and integration of legacy systems aside, the need to have proper data gathering and analysis infrastructure is another challenge. At the same time, data quality and compliance with privacy regulations are other challenges in this space.

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Predictive Models for Chronic Diseases: Transforming Healthcare

Predictive Models for Chronic Diseases: Transforming Healthcare

A major healthcare transformation is in the works, considering the growing integration of the sector with cutting-edge technologies. Along with data-driven insights and personalised medicine, there are other steps being taken for early detection of chronic diseases, such as the usage of advanced predictive models. If implemented suitably, this could herald a mega healthcare revolution in the near future.  Predictive analytics may become a tool for preventing chronic ailments, while enabling providers to swiftly detect early signs of ailments and intervene accordingly. Here is a closer look at these aspects. 1.What are disease prediction models? Disease prediction models are essentially advanced predictive models that are deployed for early detection based on data-driven insights. Machine learning (ML) models help in the swift diagnosis of chronic ailments. Those suffering from the same usually require lifelong medical aid. Here are a few other points worth noting in this regard:  2.What predictive models are used in healthcare? 3. What types of data are used in predictive modelsfor chronic diseases? There are various kinds of data used by advanced predictive models for chronic ailments. Here are a few aspects worth keeping in mind:  FAQs 1.What are the potential benefits of using predictive models for chronic diseases in healthcare resource allocation? Predictive models can help healthcare providers detect early signs of chronic diseases in patients based on diverse data points. At the same time, they can speed up early interventions and reduce the chances of disease contraction and fatalities with these insights. It will also reduce a major chunk of healthcare costs and resources allocated towards the treatment of these diseases. 2.How can predictive models contribute to cost savings in healthcare? Predictive models can help save costs that are otherwise allocated for treating chronic ailments. Early detection of signs and vulnerabilities can help facilitate strategic interventions and medical advice that may prevent these diseases from occurring. Naturally, this helps reduce healthcare costs related to treatment and resource allocation. 3.How do predictive models improve their performance with time? Predictive models keep enhancing their overall performance with the passage of time due to the nature of their algorithms. The more a provider feeds data into algorithms, the more the accuracy levels of predictive models. This helps in the generation of more accurate and helpful insights. 4.What are some of the challenges associated with implementing predictive models for chronic diseases? Some of the common challenges associated with implementing predictive models for chronic ailments include poor data quality, insufficient data, issues with accuracy levels at times due to the complexity of medical data, and technological integration.

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Transforming Banking Experiences with Generative AI

Transforming Banking Experiences with Generative AI

Digital transformation is the cornerstone of almost every industry today and banking is no exception. Generative AI and machine learning are technological innovations that are fast revamping the entire banking landscape in the recent scenario, redefining automation for financial services, personalisation and customer engagement and overall operations including risk management. Generative AI is a specialised category of AI (artificial intelligence) which helps in the generation of fresh ideas and content along with pattern-based solutions that are gleaned from pre-existing information. It is thus suitable for diverse applications throughout the banking sector. It can enable intelligent decision-making along with better risk management, fraud detection, and real-time decisions. Here is a deeper look at the same.   How is generative AI used in banking? Generative AI and machine learning can enable the analysis of huge volumes of data sets and then generate responses accordingly. Trends and patterns can be easily identified and the information leveraged to take informed decisions accordingly. Here are some of the core aspects worth noting in this regard:  Customer service with generative AI Customer engagement and service can be radically transformed with the help of generative AI. Here are some points worth noting:  Risk management with generative AI Generative AI will be a huge game-changer and harbinger of digital transformation in the near future. Chatbots and virtual assistants will steadily take over the customer support space with human resources focusing on more crucial duties. Loan processing and other duties will be streamlined and customer experiences will be more personalized and fulfilling. FAQs 1.How can banks effectively adopt generative AI technologies? Banks can adopt generative AI technologies for identifying potential frauds, managing risks, predicting future risks, and also automating customer evaluation including credit and financial history checks. Banks can also use these technologies for improving customer service and enabling higher personalization. 2.Are there any challenges or risks associated with implementing generative AI in banking? There are challenges like data privacy and the need to use synthetic data in the right manner for avoiding breaches and security hassles. Generative AI models may sometimes have higher complexity and interpretation may be tough in some cases. Maintaining transparency and adhering to legal/regulatory mechanisms are other challenges in this regard. 3.How can generative AI help banks make better financial decisions? Generative AI can enable banks to take better decisions through analyzing customer data and offering insights in real-time. Naturally, banks can take more accurate and informed decisions about sanctioning loans and other customer-facing aspects. 4.Can generative AI replace human bankers in the future? While generative AI will automate and streamline repetitive tasks in the future and possibly take care of customer communication and support, it will not be a full replacement for human beings. It will help in policy-building, decision-making, fraud detection, risk management, and personalization. However, human bankers will always be required for taking care of more crucial and complex tasks

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INT. Pulse

INT. PULSE

Dear Colleague, these days, if a tech newsletter does not start with the acronyms AI or ML, it can be safely assumed that its writers are probably living under a rock. Thus, to mask our prehistoric addresses from over 40 thousand monthly readers, we are starting June’s Pulse with an AI story, but there’s a twist. Out With It, Please. Okay, so word on the street is, contrary to the doomsayers, AI and ML seem to be creating new jobs for humans, faster than they are killing employment. No Way! Surpriiise. A good 75% of companies think that they will adopt AI in their businesses soon enough; the great challenge now is to get the staff with the necessary expertise to fill these new jobs. Many human resources folk are also suddenly agreeing that the labour market is being shaken by the demand for new workers in AI-related areas. Where Are These New Jobs? Alright, we will start you off with two.  For instance, AI can create personalised medicinal treatments, precision farming and sophisticated industrial methods. These new products and services can lead to new responsibilities in research, development and marketing, along with new skills and experience requirements.   The emergence of AI-powered digital assistants and smart home appliances has opened up new career prospects for hardware engineers, data analysts and software developers, akin to how autonomous vehicle and drones have opened up new career prospects for engineers, technicians and logistics specialists. Always A Catch While more employment is always cool, AI is expected to make some jobs obsolete too, especially in the content generation area.  This means, pretty soon, as generative AI begins to write this newsletter, you may receive INT. Pulse twice a day instead of once a month, and we will have retired to the Himalayas.  Win-Win  STATS: When Every Analytical Tool Failed Sample this.  On 14th June, Sweden reported unexpectedly high inflation for May, causing economists and all their tech tools to wonder: What on earth kept prices that high? (Side Talk: Dealing with an analytical crisis? Solve it over a  with Dipak Singh, our analytics & AI head honcho). And then it dawned upon them like a Manali sunrise: Beyoncé.  The pop superstar put her Renaissance tour on the road in Stockholm last month, pulling 80,000+ fans to the city over two nights.  Danske Bank finally deciphered that for this influx of concertgoers, hotels and restaurants upped their prices to such a degree, it skyrocketed overall inflation.   Your TakeawayThe fact that one person, by the sheer force of her popularity, was responsible for higher inflation in an entire country is…beyond analytics.  Danske’s chief Swedish economist, Michael Grahn, quoted, “It’s quite astonishing for a single event. We haven’t seen this before.” Well, now we have.  GA4: Underreporting Traffic? No Papa Alright peeps, the Universal Analytics (UA) sun is finally setting and come July, Google Analytics 4 (GA4) is all we have, making migration to this new platform mandatory. But, but, but… with GA4, you may also be saying hello to underreported website traffic. Before you start comparing it to the train wreck some recent Windows upgrades were, we advise you to read on. What Happened Here? Indian proptech giant, Square Yards, usually gets ~70% of its web traffic from mobile devices and the rest from Desktops/tablets. But on switching to GA4, they found a hot-potato drop in traffic stats. On fishing in deeper waters, the folks at SY found that mobile traffic was under-reported by the GA4 tracker. This could also be-happening/happen to you. GA4, Give Me Everything Please So, there’s this important setting in the GA4 Console that allows Google to collect metadata about granular device details of your site and app visitors, so it can provide you with location and device-based info. This is turned OFF by default for any new property being created in the GA Dashboard. 🤷🏻‍♀️ Fix? 1️⃣ Go to your GA4 property settings 2️⃣ Select Data Collection, and 3️⃣ Enable Granular location and device data collection. Aaaand, you’re done. 📌 We are keeping our crawlers active on GA4 stories for a ‘best hacks and tips blurb’ in our July edition. Btw, if you need help with GA4 migration, or perhaps, take GA4 to its optimum limits to power growth for your business, Sanjeeb and his team are all set to help you out. Reach out to Sanjeeb here. AI/ML: How Nvidia Hit A Jackpot Selling Chips We’ve all heard of cashing in your chips post winning big but selling chips to hit a jackpot? That’s a new one and the trophy goes out to hardware giant, Nvidia. How Come? 1️⃣ For starters, you should know Nvidia (USD960B)* is now worth more than: *as on May 27, 2023. 2️⃣ This is the company that started 30 years ago and was for almost all its life, only a video game chip maker. 3️⃣ You should also know that over three decades, Nvidia was on the verge of bankruptcy 3 times. How On Earth Then……? It turns out Nvidia’s GPUs (originally created to improve gaming graphics) are also well suited for the data processing and model training demands of generative AI. Like this analyst said, “Training AI models demands chips that have large memory…Nvidia is the only company making those chips.” The rest is history (in the making). ­Stuff We Are Watching 📌Instant Productivity Boost: You know what’s hot? Bring your own device (BYOD) programs are, as they can potentially save organisations big money on equipment – and they might increase productivity.. but there are flip sides too. 📌 A Funding Tip That Works: CEOs and CFOs looking for funding? Remember, when framing your competition to investors without having to buy them antacids or anti-stress pills, follow this hierarchy of competition, from most worrisome to least. 📌 Are Cookies Dying? In one word? Yes. In fact, we may have only 50-odd weeks left. That’s the estimate for cookie-pocalypse. Educated guesses we gathered from all over indicate that Google will get rid of 3P cookies in Chrome around

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Smart Insurance: Exploring Blockchain and AI Integration

Smart Insurance: Exploring Blockchain and AI Integration

Smart insurance has increasingly become representative of the evolutionary headwinds in the industry. Digital transformation has been a key feature of the sector for the last few years, especially with the integration of AI insurance and blockchain insurance technologies. Insurtech is no longer an exception as well. These digital advancements are boosting customer experiences while enabling better risk management, claims processing and fraud detection at the same time. Insurers are completely redefining consumer engagement, while moving towards greater personalisation simultaneously. A McKinsey report estimates that 25% of the insurance sector will be fully automated by the year 2025, backed by AI, ML and blockchain, among other technologies. Here’s taking a deeper look at the same. How Blockchain and AI are Changing the Insurance Industry AI insurance technologies are completely revolutionising the landscape. The same can be said for blockchain insurance solutions. Here are some core points worth noting in this regard. The Benefits of Blockchain and AI for Insurance As expected, there are multifarious advantages offered by AI and blockchain in the insurance industry. Some of them include the following:  The Future of Smart Insurance What does smart insurance look like in the future? Here are some ways in which digital transformation can completely reshape the insurance industry: FAQs 1.What role does artificial intelligence play in smart insurance? Artificial intelligence has a vital role to play in smart insurance, automating repetitive tasks including claims submissions, processing, and more. It also helps detect fraud, assess risks, and enhance customer experiences through Chatbot-based communication. 2.What are some real-world examples of smart insurance applications using blockchain and AI? Some real-world examples include micro-insurance, parametric insurance models, usage-based insurance, fraud detection, data structuring through Blockchain and IoT and also multiple risk participation or reinsurance. 3.Are there any challenges or limitations to consider when implementing blockchain and AI in smart insurance? Blockchain networks may require higher computational capabilities for transaction validation. Other challenges for AI and blockchain implementation include data quality, digital adoption, integration of legacy systems, and more. 4.How does the integration of blockchain and AI in insurance impact customer experience? Customer experiences are automatically enhanced through the integration of blockchain and AI. Their information remains tamper-proof and secure, while they can swiftly be onboarded and file/process claims without hassles. They can get quicker automated responses to queries along with personalised recommendations. Claims processing timelines are also greatly reduced due to these technologies.

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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.

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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.

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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.

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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.

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