Tag: INT

Strengthening Cyber security in BFS: Addressing the Challenges and Risks for a Secure Future

Boosting cyber security in the BFS (banking and financial services) sector is of the utmost importance. With the increasing cyber-attacks on these institutions and their widespread shift towards digitized financial services, caution is the name of the game at this juncture. It is a reality that the BFS industry is under higher threats these days, becoming arguably the highest-targeted sector for cyber crooks. There is always a need for prompt threat detection along with robust network security to combat risks like data breaches and digital banking threats. Credit-card threats are also on the rise in recent years. Malware as a service or MaaS is another unfortunate and problematic trend of launching malware attacks. DDoS (distributed denial of service) attacks are also issues for BFS players, where a compromised PC network is leveraged for creating a huge number of false requests to the systems of the platform or bank, thereby leading to a disruption in operations and leaving them unable to respond to consumer requests of a legitimate nature. This naturally makes cyber security a necessity for the BFS industry. Cyber security in the banking and financial services sector and associated aspects Cyber security is the collection of protocols, technologies, and other methods which help guard against damages, attacks, viruses, malware, data thefts, hacking, and unauthorized access to devices, networks, data, and programs. Safeguarding user assets is the key objective in this case, while upholding data privacy and adhering to data safety regulations simultaneously. Digital payments, debit and credit card usage, wallets, and other cashless means of transactions necessitate cyber security measures. Data breaches are not only damaging for customers, but also costly for financial institutions. Cyber frauds or attacks also lead to a huge amount of time and energy being spent in recovering from the same. Inappropriate usage of private data may also be damaging in a larger context, since user information is usually sensitive. Cyber security measures are necessary to prevent issues like phishing attacks, Trojans, spoofing, ransomware, and more. Here are some applications that are worth noting: Network tracking is continually scanning networks for signs of any intrusive or dangerous behaviour. It is frequently used in tandem with other security measures like IDS (intrusion detection system), antivirus software, and firewalls. This software enables either automatic or manual tracking of network security. The application security guards applications which are vital for business functions. This come with features like code signing and listing, while enabling synchronization of the security policies with requisite file-sharing permissions and also multi-factor authentication. AI is now playing a vital role in cyber security, enabling better authentication or verification protocols. Financial cyber security involves data integrity, risk management, risk analysis, and security awareness training. Some other core aspects include evaluation of risks and prevention of harm from the same. Data security also takes care of ensuring the security of sensitive data. Wide-area connections enable prevention of attacks for huge systems, while adhering to rigorous safety protocols. It continually tracks all vital programs and undertakes security checks for servers, users, and networks. Challenges while implementing cyber security in BFS frameworksHere are some of the hurdles that have to be overcome, while implementing cyber security measures in the BFS industry: FAQs

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Blockchain beyond cryptocurrency

Blockchain beyond cryptocurrency

Blockchain has revolutionised new-age applications including cryptocurrency. But what are the applications of blockchain outside of cryptocurrency? We all know about the application of blockchain in cryptocurrency but what about its usage beyond this arena? Here’s looking at the possibilities. A simple guide to blockchain Blockchain is a distributed and shared ledger that enables decentralised control. A blockchain comprises blocks or units that are linked through chains. Every chain has encrypted data that is made up with data from the earlier block to build the entire network. Blockchains are available as both private and public ledgers and specific implementation procedures enable any party to take part while the others will require access and invitation rights. Some of the major rights of blockchain include provenance, encryption and immutability. There are no limits to the kinds of businesses and industries that may benefit from using blockchain technology. Applications of blockchain technologies Blockchain technology has diverse applications, right from digital identity and digital voting to use cases in the healthcare industry. Bitcoin is already expanding its presence throughout the global finance segment while smart contracts are also being used as replacements for escrow and also for managing digital identity. Public blockchains are already available, enabling any individual to participate while corporate blockchains leverage private ledgers, thereby restricting authorisation and access alike. Financial services are already considering the utilisation of blockchains with its immutability and security being favourable for meeting needs in both insurance and banking. Healthcare companies are already using them to store health data or records while open-source versions of databases are already enabling better access to patient data, thereby enabling superior coordination and communication. FAQs This technology enhances cybersecurity greatly with its core aspects of decentralisation, consensus and cryptography, thereby enabling higher transaction-based trust. Blockchain ensures security control at the highest level for ensuring data confidentiality. Encrypted data in the blockchain makes sure that threats are mitigated without hackers retrieving or reading information in a suitable manner. Digital identity is enabled immaculately by blockchain technology. It is a self-sovereign identity that cannot be altered and offers more security than conventional systems of identity. It can fully transform the manner in which identities are used for linking to various online services. Blockchain makes sure that this information can be easily traced, audited, and verified, in a matter of seconds. Individuals can curate their personal profiles and control the sharing of their data. Issuers can also verify credentials swiftly with these technologies. Digital voting is a procedure where voters can leverage a technology-centric process for voting, minus some of the conventional issues of the voting process. The blockchain-based system is fully decentralised and open, while ensuring higher protection of voters. Voter identity stays anonymous and there are decentralised nodes available for electronic voting in this system. Some of the inherent challenges to the implementation of blockchain beyond cryptocurrency include concerns regarding privacy and security. Some other issues include scalability, interoperability, security, consumption of energy and higher complexity levels.

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Insurtech Revolutionises Insurance with Personalised, Faster, and Affordable Solutions

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

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Ethereum Virtual Machine (EVM) & Smart Contract

Ethereum Virtual Machine (EVM) & Smart Contract

The Ethereum Virtual Machine (EVM) has set the ball rolling for smoother smart contracts within the entire ecosystem. What is the difference between Ethereum and smart contract? The former is a blockchain platform that is decentralised and sets up a peer-to-peer network which verifies and implements smart contracts safely.  Smart contracts, on the other hand, are mechanisms for enabling participants to securely transact without the need for any trusted central authority. Here’s learning a little more about smart contracts before diving into EVM. Smart Contracts- How they work What is the objective of smart contract? It is a self-implementing program automating the actions needed for a contract or agreement. Upon completion, the transactions can be tracked and are not reversible. They enable agreements and transactions to take place among anonymous participants without the need for any central governing authority or enforcement procedures. What is EVM? The Ethereum Virtual Machine (EVM) is a runtime ecosystem for smart contracts in Ethereum. It is isolated and sandboxed from the other system components. Your programs and data will remain safe and unaffected by other EVM operations, irrespective of the number of times you call any specific function on the same. Ethereum has its own scripting language (Turing-complete) which is known as Solidity and this makes it necessary to implement the code. The EVM takes care of this necessary task. The EVM has been created with an objective of becoming a world computer and has massive power, executing scripts for generating outcomes which are arbitrary. It stores blockchain data while executing code in the smart contracts on the network.  The machine can easily run any type of crypto-based contract which is built on Ethereum. The EVM enables a platform for decentralised programs/applications to be built over it, making sure that all smart contracts and transactions are done in the right way as necessary for the smart contract code. It also functions as an application execution platform. How does the EVM work? Some of the major parts of the Ethereum Virtual Machine include the following: Actions-These are basic functions to be performed on the assets that are stored on the system, including addition and multiplication. Balance- This is the Ether amount that you can possess at any point of time. Block- This is the immutable action and transaction storage linked to Ethereum for the lifetime. The blocks are only 65,000 in total. Blockhash- This is the hash of the specific block and the data stored in the same. Block Number- This number identifies the block where any specific Blockhash belongs. It begins from zero and goes up each time any new block is added to the entire chain. Code- The code that is executed in the EVM helps determine the action that will result from an input taking place. CodeHash- It refers to the hash of the specific code. This number may change with the change in the code, on the basis of inputs. CodeSize- It indicates the real size of the code (bytes). GasLimit- It is the EVM part which enables users to specify the gas they are okay with spending for the execution of anything. If the number stands at zero, then nothing will take place, although such a scenario is rare. Summing Up The Ethereum Virtual Machine has its own set of benefits. It helps implement un-trusted code without any risks to data in the process. The computations will never interfere with any other events across the system. Complex smart contracts may be run easily on the system without being anxious about their interactions. The contracts written here will get access to all the states of Ethereum anytime they wish, enabling processing in a deterministic manner and ensuring higher guarantees about their accuracy.  The distributed consensus model is followed where the same program is run by each participant from his/her own machine. The network has to reach consensus at any point in time, gaining more robustness against individual node failures. Multiple node updates can take place together without any worries on disagreements on the code writing. The EVM is also tailored to write smart contracts along with helping develop decentralised applications that are programs running across distributed networks in a manner where each one witnesses the same version. This makes it easier to write stateful contracts which require access to any type of persistent storage.  FAQs 1. What programming languages are used to write smart contracts for the EVM? The programming language for writing smart contracts is Solidity. 2. How does the EVM execute smart contracts? The EVM implements tasks and function calls to smart contracts through the Opcodes instructions interpretation although data formatting takes place in byte-codes. 3. What are some advantages of using smart contracts on the Ethereum blockchain? Some of the advantages include higher autonomy, lower costs, higher transparency, automatic updates, and greater speed of transactions. 4. What are some limitations of the Ethereum Virtual Machine (EVM) and smart contracts? The EVM may be unable to access real-world data which is one of the limitations. Also, it is an isolated and sandboxed environment, which means that its code will not get access to any other file systems, processes, or the network.

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Machine Failure and Predictive Maintenance through analytics in Insurance

Predictive maintenance and detecting machine failures is possible with the help of predictive analytics in the insurance sector The figures could increase considerably over the coming years, with the sheer value of predictive analytics being demonstrated through numerous applications and use cases.  Equipment insurance and the role of predictive analytics There are several machinery breakdown and equipment insurance products that are available throughout the spectrum today. This is where machine failure predictions come into focus, since predictive analytics can tap sensor data analysis and risk mitigation models for coming up with unique insights that can be used by insurance companies positively. Some insurers also offer strategic riders for the coverage of additional equipment risks or things like machine foundations, air freight, costs, and customs duty among others.  Insurance policies ensure coverage for losses emerging from damages due to both external and internal causes. Some of them may be structural issues, short circuits, absence of lubrication, and a lot more. Insurance companies have to provide coverage for both partial and total losses. When it comes to the claims procedure for this type of insurance, predictive analytics can enable better machine failure predictions, enabling insurance companies to predict their claim payouts or the likelihood of claim payouts through sensor data analysis and other data. Predictive maintenance tips can be deployed for consumers to avoid these breakdowns and save the insurance company’s financial obligations alongside. Owners and OEMs can also take all necessary precautions with predictive maintenance and machine failure predictions, avoiding the skyrocketing costs of equipment breakdowns/damages. Predictive models can help estimate the probabilities of failures, while also offering the capabilities to plan out maintenance in a way that losses are minimised. The second way is to optimise overall inventory, while maintaining crucial stocks for the future. How does it help OEMs? Breakdowns may also impact OEMs, while harming their reputation and also lead to the loss of business. In case any vital item is unavailable nearby, then customers may not always hesitate to procure the same from markets locally. At the same time, manpower may not always be available for immediately repairing the machine in question. These are issues that may be bypassed with predictive analytics. Dealers, OEMs, and other manufacturers can plan out their maintenance on the basis of these insights. Insurance companies can plan structures for rewarding customers who undertake the same for higher safety and lower possibilities of raising claims in the future. These models also help OEMs unveil newer revenue models for maintenance contracts. This also ensures that customers do not purchase spare parts across local markets. OEMs can also steadily enhance their offerings with these systems, with models indicating the key aspects behind the failure of components and what contributes towards their overall life in the long run. Upon the identification of issues, data is collected for necessary analysis. After data collection, the other procedures start, including visualisation and cleaning. The entire procedure leads to insights which can help predict when machines require periodic maintenance in order to avoid future mishaps and breakdowns. FAQs Machine failure may impact insurance claims greatly, since companies have to pay out either partial or total losses, depending on the terms and conditions. Predictive analytics can help a great deal by analysing sensor data and other sources, predicting the chances of machine failure. This will help companies implement predictive maintenance strategies and prevent breakdowns. The benefits of predictive maintenance in insurance include the ability to forecast future machine failures and breakdowns, deploying predictive maintenance tips for preventing the same, lower chances of paying out claims, and higher cost savings not just for insurers, but also OEMs and companies. Insurance companies can assist their clients in the implementation of predictive maintenance blueprints through issuing tips and recommendations based on data gathered through predictive analytics.

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The impact of social media on BFS and the potential for social media analytics to inform marketing and customer engagement strategies

The Impact Of Social Media On BFS And The Potential For Social Media Analytics To Inform Marketing And Customer Engagement Strategies

The impact of social media on the BFS (banking and financial services) industry cannot be underestimated today. Social media adoption has earlier been slower in this segment due to several concerns relating to compliance, perception, and industry regulations along with anxieties regarding reputation. The industry was hitherto regarded as conservative. However, banking and financial services entities are venturing into social media for tapping its sheer potential in recent years. Many retail BFS players are now developing their digital presence via mobile banking and other digital applications, in addition to building omnichannel touch points for customers through social media platforms. Social media analytics is redefining customer engagement and driving marketing strategies for BFS firms. With AI and machine learning for the analysis of innumerable data points garnered via social media platforms and combining the same with existing customer database intelligence and reviews, brands are steadily tapping data-driven insights which are shaping their future products. The importance of social media for banking and financial institutions Digital marketing and target marketing campaigns executed by banking and financial services players are now increasingly being shaped by social media analytics. Consumers are spending more time on social media platforms and they are steadily becoming ideal avenues for raising awareness, disseminating consumer education, and enabling potent customer engagement. Reports indicate how leading banks in the U.S. scaled up their overall digital followings by a minimum of 30% (quarterly basis) last year, across social media platforms like Twitter, Instagram, and YouTube.  From tapping customer metrics to doing research on prospects in the enterprise space, forecasting consumer trends, and backing data-based innovation, social media analytics is transforming the entire rules of marketing and outreach for BFS firms. How social media analytics helps BFS entities Social media analytics is immensely helpful for banking and financial services institutions in the following ways:  Leveraging consumer intelligence- Social media analytics helps greatly in terms of evaluating both external and internal datasets across platforms and touch points. Social data insights are also being tapped more efficiently and shared throughout multiple teams, with real-time access to consumer feedback and trends in conversations.  Easy data aggregation and analysis- Social media analytics enables easier aggregation of unstructured information throughout social networks and objective data analysis for enabling enterprises to take better decisions. Huge chunks of data are swiftly processed through automated social media tools, thereby helping companies build superior communication channels with customers and other stakeholders, while ensuring better opportunities for cross-selling and providing better customer experiences.  Sales and Marketing Impact- Social media analytics ensures an in-depth study of consumer behavioral trends, preferences, and conservations. Institutions can easily structure their new services and products, brand marketing and promotional initiatives, announcements, marketing blueprints, and advertising plans for higher brand alignment and equity.  Developing new products- Analytics can also tap consumer data and preferences, helping large enterprises develop new services and products for meeting particular needs of targeted customers.  Customer service and support- Several institutions can understand how effective their offerings are, through an analysis of feedback on social media platforms. Banks and financial services institutions may leverage available feedback for making sure that all grievances are swiftly addressed, thereby enabling higher retention and satisfaction levels of customers.  Managing risks– Social media analytics can also enable institutions to adopt a more pro-active approach towards handling their risks. Through the analysis of data across social media platforms along with consumer behavior, they can evaluate and manage their risks in a more effective manner.  Banks and financial institutions have multiple advantages to harness through social media analytics. Financial firms can gain multiple advantages through integrating these tools into their  business blueprints. They can also become more customer-focused entities that can reach out to their audience base with specifically targeted products and services.  At the same time, they can also analyze or revamp strategies for businesses on the basis of customer preferences and feedback. They may also boost overall customer experiences, while tackling and tracking risks in a more pro-active manner. It is time that financial institutions should incorporate social media-based strategies into their strategies in the near future.  FAQs What social media platforms are most relevant to the BFS industry, and how can they be leveraged for marketing and customer engagement? Social media platforms like Twitter, Facebook, Instagram, and YouTube are highly relevant for the banking and financial services industry. They can be tapped with social media analytics in order to generate consumer feedback, insights, and preferences, which can help drive marketing and customer engagement strategies.  What regulatory considerations should BFS companies be aware of when using social media analytics for marketing and customer engagement? There are several global regulations instituted throughout several countries and regions. These include the fair lending act or equivalent regulations along with other ethical considerations as outlined by the authorities.  What are some emerging trends and technologies related to social media analytics that BFS companies should be aware of? AI-based content, integration of social media metrics into KPIs of companies, and business intelligence are major emerging trends and technologies linked to social media analytics. These are trends that BFS entities should be aware of.  How can social media analytics be used to identify and address customer complaints and concerns in real-time? Social media analytics can be used for identifying customer grievances and complaints on a real-time basis. It can be used to address and respond to these concerns in real-time as well. This can be done through analyzing voluminous data across social media platforms. 

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The rise of insurtech startups and the implications for traditional insurance companies

The Rise Of Insurtech Startups And The Implications For Traditional Insurance Companies

Insurtech startups have managed to carve a sizable niche for themselves in the global industry sphere. They are at the root of the disruption in the insurance industry that has made traditional insurance companies sit up and take notice. Insurtech startups are technology driven companies which are foraying into the insurance segment, offering greater innovation in insurance products, services, and experiences, while offering easy coverage options to a growingly tech-savvy customer base. While they have not managed to obtain significant market share till now, it is evident that they represent the future of the insurance industry in a manner of speaking. This is what insurance companies in the conventional realm should monitor in order to stay ahead of the competition. The growth of insurtech The rise of insurtech startups has mainly been attributed to the digital transformation in insurance that they have made mainstream, in a manner of speaking. At the same time, they have enabled more customer-centric insurance products and improved service, while saving both time and money. These are aspects which are contributing to their rapid rise throughout the world. Here are some aspects that are worth noting in this context: What is the X-Factor offered by insurtech startups?  What lies behind the disruption in the insurance industry, brought about by insurtech startups? What is their X-factor in a manner of speaking? Well, it is a combination of factors in reality. Some of these include the following:  So where does the traditional insurance sector go from here? Experts feel that going forward, there will be more collaborations between traditional and insurtech companies. Brick-and-mortar traditional players are already experimenting with digital platforms and innovative solutions for retaining their customer base. They will increasingly want to reach out to insurtech startups for leveraging their technological expertise, while offering the reliability and brand value that they bring to the table. This could become a dominant trend going forward. Otherwise, the disruption that is afoot, could eventually see insurtech gaining ground as a concept itself, something that is already taking place worldwide.  FAQs What is actuarial science and how does it relate to analytics? Actuarial science is all about the assessment of financial risks in finance and insurance, with the use of statistical and mathematical methods. Actuarial science can apply analytics in order to classify, evaluate, and predict uncertain and future events. The assessment and identification of probable losses/risks can be accomplished by the integration of analytics into actuarial science.  How can analytics be used in actuarial science? Analytics and data science can use multiple techniques within the broader paradigm of actuarial science to make informed and accurate predictions about probabilities of risks. Some techniques include recognition of patterns, visualisation of data, and statistical modeling. What are the benefits of using analytics in actuarial science? Analytics makes actuarial science-related underwriting functions more efficient, enabling faster and more accurate visualisation of data, evaluation and identification of probable risks, recognition of vital patterns, and more accurate pricing/risk assessments. What are the challenges of implementing analytics in actuarial science? Technological integration and lack of awareness/knowledge about analytical tools are the major challenges towards the implementation of analytics in actuarial science. Also, the quality of data is another aspect that has to be taken into account. Are there any examples of successful implementation of analytics in actuarial science? One example could be using data analytics in actuarial science to evaluate the historical and present data of a customer along with identifying patterns in behavior and other aspects for calculating a fair insurance premium as per his/her risk levels. 

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INT. Pulse

INT Pulse

Dear Colleague, there you are at your desk, starting the day with a review of your 80/20 list and suddenly –  upcoming meeting alert – or in other words, the sound of your workday dying.  Fret not, because just like you, your boss hates that sound too.  Nope, we didn’t cook this up ourselves – multiple researches stand by what we’re telling you.   Executives spend an average of 25 hours a week in meetings, yet nearly half of those video calls and project updates could disappear without any negative impact, per a survey of over 10,000 desk workers by Future Forum.   Reluctantly going to noncritical meetings wastes about USD100 million a year at big organisations, according to another survey.  The studies found the top reason why business leaders went to unproductive meetings is that they thought it would be a good use of time, but ultimately wasn’t.  They also attend because they’re afraid to miss something important, and to show their own manager they’re working.   Reminds us of that old saying – ‘this meeting could have been an email’.  Just like the one you are reading now.  AI: Why Google Is Taking It Nice And Easy What You Know The once-a-little-known startup, OpenAI, took on Big G head-on in a fight for the spot as AI’s top dog.  Within just 60 days of its release, ChatGPT amassed 100 million+ users worldwide. Also, since it saw daylight, ChatGPT has passed multiple prestigious graduate-level exams in law and business, even going as far as passing the United States Medical Licensing Exam (USMLE).  On the flip side, Google recently released an ad for Bard (it’s own AI tool) that had incorrect information coming directly from the chatbot, resulting in the loss of over USD100 billion in market value for the tech giant. What You Also Know Even with an unspectacular tech stack, ChatGPT’s decision to offer AI to the masses through the web has revolutionised text generation through automation, having big ramifications on sectors like education, employment, and, particularly relevant to Google, the evolution of online searches.  On the other hand, Google has only allowed some groups to test out Bard before its full public release in the near future.  Plus, Blake Lemoine, an ex Googler, stirred up a storm by publishing a document in which he proposed the possibility of the AI being sentient. (ChatGPT had its share of lobotomy as well, BTW) What You Don’t Know ChatGPT might be winning the AI race for now, but soon Sam Altman will probably have to fly to Washington DC and spend afternoons with an 85-year-old farmer-turned-Senator from Idaho, to explain why his great-granddaughter was suspended from private middle school for using something called “the GPT AI.”  Jokes apart, our resident AI/ML lead, Dipak Singh, is doing some transformational work for enterprises with ChatGPT, Analytics and Artificial Intelligence in general. Reach out to Dipak to explore a solution for your organisation BFSI: Apple Is Ummm, A Bank Now? For all practical considerations, yes.  Let us explain. Sometime back, Apple drove an armoured cash van through the American banking industry.  Yeah?  True. While the average bank is paying less than 0.5% on savings accounts, the USD2.6 trillion tech giant announced it would dish out 4.15% (that’s 10X the national average) annual returns to savers. This, when regional American banks are balking in the wake of the Silicon Valley Bank crisis to maintain their deposit bases, and cash-starved fintech startups are gasping for breath.  Is It A Gravity Game Changer? Pretty much. Per Forbes, “as trust in traditional banks falters, the two most iconic names in tech and finance are joining together to create what might become America’s mightiest FinTech.”  Clarification on the other iconic name – since Apple does not have a banking license, it has teamed up with Goldman Sachs Bank, USA.  In pure fintech jargon, Apple is now a neobank like Jupiter and Fi Money – except its ginormous brand strength, with over two billion iPhones globally, is now serving as Goldman’s branch network.  At 4.5%, Where Are The Profits? Apple’s 10X returns savings account is less about profits than it is about bringing more iPhone owners under Apple and Goldman’s financial umbrella.  While two billion people around the world own Apple devices, fewer than 10% are Apple Card users, meaning there is a megatron* market opportunity waiting to be tapped already.  Net earnings from interest margins may not be Goldman’s priority either.   Profits or no profits, the iPhone user is certainly not complaining.  *megatron is a myth, but it sounds so cool, we used it for effect.  Pharma: Unable To Pear The Loss Pharma technology pioneers, here’s a reality check – one that is brought to you, courtesy, insurance companies.  What The Eff? Yes, Pear Therapeutics, creator of 3 FDA-cleared prescription apps to help treat substance use disorder and insomnia, just announced that it is, err, bankrupt, as the tech startup struggled to get insurers to pay for its technology.  Btw, we are talking about America here.  While doctors were willing to prescribe digital therapeutics and patients were willing to use them, “that isn’t enough,” Pear’s CEO Corey McCann wrote in this LinkedIn post.  “Payors have the ability to deny payment for therapies that are clinically necessary, effective, and cost-saving.” What made Pear special? Clinical robustness: Through high quality clinical trials, Pear demonstrated enhanced patient outcomes in substance use disorder and insomnia. Regulatory blessing: One of the earliest to get US-FDA approval, Pear saw 10,000+ prescriptions written for its digital solution. Investor enthusiasm: Pear raised USD300M in equity, USD100M in debt and went public last year with a valuation of USD1B+. Key Takeaway? Per Tushar Sadhu on LinkedIn, “external capital comes with its powers and responsibilities. Unrealistic valuation and pre-mature IPO undid the good work the company had done in product creation.”  Stuff We Are Watching  GoI’s Chatbot Plan: The Government of India is working to create a multilingual ChatGPT-like chatbot helpline that can be used to manage grievances of disgruntled consumers.   USD100,000 Saved by AI: ChatGPT use cases now run into millions, populating every nook and corner of social timelines, but how does

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The benefits of adopting telematics in auto insurance

The Benefits Of Adopting Telematics In Auto Insurance

Telematics systems have become mainstream throughout the automotive industry. Many experts feel they have the potential to completely revolutionise and transform the sector. Based on reports by Bloomberg NEF, close to 1.2 billion cars were plying in the year 2022, while this could reach a staggering 1.5 billion vehicles by the year 2039 as per estimates. This naturally calls for a revamped auto insurance mechanism, which takes things like driver behavior into account. This has been an offshoot of the usage-based insurance model in the automotive insurance space. Due to the continual increase in car volumes, the global auto sector is poised to touch a staggering USD$1.4 trillion in revenues by the year 2040. This will be backed by stringent regulations worldwide which make insurance coverage compulsory, along with systems for tracking and penalties, in order to scale up auto insurance penetration throughout owners. In a more traditional context, insurance companies usually emphasised upon things like the vehicle age, location, and motor vehicle reports for working out the premiums and risks.  However, telematics systems are now enabling the evaluation of driving habits with a view towards more effective estimates of risks and pricing. These programs are increasingly driven by technologies like IoT (Internet of Things) and data analytics, thereby becoming disruptors for the segment in recent years. If you look at it objectively, North America is already the biggest market for telematics-driven insurance, with close to a whopping 22 million policies active from top companies. The global market for telematics systems in insurance should touch USD$6.2 billion in 2025, indicating 22.7% in CAGR (compounded annual growth rate) as per Grand View Research reports. What does this tell us? Telematics is here to stay.  What is telematics and how is it relevant in insurance? UBI (usage-based interface) or telematics fuses informatics and telecommunications, which is the foundation for data processing, with an aim towards retrieval and storage.  Insurers put tracking devices into vehicles which receive, store, and send telemetry information/data on the onboard diagnostics of the vehicle, enabling wireless communication. Vehicle-based data is collected, including the location, speed, harsh braking, idle time, and fuel consumption. This is given on a real-time basis to car owners and insurance companies via tracking devices or smartphone apps. What are the biggest advantages of telematics? Some of the top advantages of telematics systems in vehicles include the following:  Insured policyholders can lower their premium costs through the adoption of safe driving practices/habits.  Insurance companies can better analyse risks of possible accidents and predict the possibilities of claims in the future.  Insurance companies can provide rewards and value-added incentives for scaling up customer retention, loyalty, and satisfaction.  Insurance companies have a much fairer and more effective method of risk and premium estimation with telematics.  Telematics offers valid and accurate data regarding the vehicle functions and driver behavior, which ensure actionable insights for processing claims. It also contributes towards reducing any fraudulent claims or losses.  Insurance companies are seeing the evolution of models like distance-based/pay how you drive/pay as you drive/control your drive insurance options.  Dashboard cameras in tandem with telematics can help insurers gain better insights on the reasons behind accidents and get more knowledge of the same.  Telematics in insurance removes several hurdles throughout the supply chain, right from underwriting and claims management to serving customers.  These devices also lead to higher awareness and alertness amongst drivers who wish to improve their driving behavior, patterns, and scores. Hence, it may contribute towards lower accidents on the road.  These technologies may contribute greatly towards lowering crime rates globally.  Telematics can thus be a major boon in the auto insurance space, with huge potential not just for personal vehicle insurance, but also for fleets and logistics players. It offers more transparency in premium and risk evaluation, while lowering the chances of accidents and other mishaps. It will keep evolving gradually throughout the world, until it becomes an accepted form of auto insurance. At the moment, it is steadily being recognised and implemented by insurance companies and should have its boom moment in the near future. FAQs How can telematics be used in auto insurance? Auto insurance companies can use telematics to determine the driving behavior and vehicle operations of policyholders, using actionable data for evaluating risks and premiums. Telematics can also offer higher insights on mishaps and accidents, thereby helping with claims management.  What are the benefits of using telematics in auto insurance? Telematics helps in accurately estimating risks of policyholders and their premiums. Good drivers get incentivised with lower premiums and rewards. At the same time, fraudulent claims and losses are minimised with this system.  What are the challenges of implementing telematics in auto insurance? Some of the challenges include data privacy and other regulations, since location-based information is received, stored, and shared. Other challenges include technological integration and awareness-building.  Are there any examples of successful implementation of telematics in auto insurance? Some examples of telematics-based auto insurance models include control your drive, pay as you drive, pay how you drive, and distance-based insurance.

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Data analytics plays a crucial role in clinical trial design and analysis by providing valuable insights into the effectiveness of new treatments and therapies.

The role of data analytics in clinical trial design and analysis

What is the role of data analysis in clinical trials? Can there be better clinical trial data analysis using R and other technologies? Is there a case for using big data analysis in clinical trials? Experts would certainly say Yes to all these questions. Clinical trials themselves have gone through sweeping changes over the last decade, with several new developments in immunotherapy, stem cell research, genomics, and cancer therapy among numerous segments. At the same time, there has been a transformation in the implementation of clinical trials and the process of identifying and developing necessary drugs.  To cite a few examples of the growing need for clinical trial data analysis, researchers gain quicker insights through the evaluation of databases of real-world patient information and the generation of synthetic control arms, while identifying drug targets alongside. They can also evaluate drug performance post-regulatory approvals in this case. This has lowered the cost and time linked to trials while lowering the overall burden on patients and enabling faster go-to-market timelines for drugs too.  What is driving data analysis in clinical trials?  Clinical trial data analysis is being majorly driven by AI (artificial intelligence) along with ML (machine learning), enabling the capabilities of collection, analysis, and production of insights from massive amounts of real-time data at scale, which is way faster than manual methods. The analysis and processing of medical imaging data for clinical trials, along with tapping data from other sources is enabling innovation of the entire process while being suitable for supporting the discovery procedure in terms of quickening the trials, go-to-market approaches, and launches.  The data volumes have greatly increased over the last few years, with more wearable usage, genomic and genetic understanding of individuals, proteomic and metabolomic profiles, and detailed clinical histories of patients derived from electronic health records. Reports indicate 30% of the data volumes of the world are generated by the global healthcare industry. The CAGR (compound annual growth rate) for healthcare data will touch 36% by the year 2025 as well. The volume of patient data in clinical systems has already grown by a whopping 500% to 2020 from 2016.  Data analysis in clinical trials- What else should you note?  Here are a few factors that are worth noting:  Synthetic control arm development  The role of data analysis in clinical trials is even more evident when one considers the development of synthetic control arms. Clinical drug discovery and trials may be fast-tracked while enhancing success rates and designs of clinical trials. Synthetic control arms may help in overcoming challenges linked to patient stratification and also lower the time required for medical treatment development. It may also enable better recruitment of patients through resolving concerns about getting placebos and enabling better management of diverse and large-sized trials.  Synthetic control arms tap into both historical clinical trials and real-world data for modelling patient control groups and doing away with the requirement for the administration of placebo treatments for patients which may hinder their health. It may negatively impact patient outcomes and enrolment in trials. The approach may work better for rare ailments where populations of patients are tinier and the lifespan is also shorter owing to the disease’s virulent nature. Using such technologies for clinical trials and bringing them closer to end-patients may significantly lower the overall inconveniences of travelling to research spots/sites and also the issue related to consistent tests.  ML and AI for better discovery of drugs ML and AI may enable a quicker analysis of data sets gathered earlier and at a swifter rate for clinicians, ensuring higher reliability and efficiency in turn. The integration of synthetic control arms in mainstream research will offer new possibilities in terms of transforming the development of drugs.  With an increase in the count of data sources including health apps, personal wearables and other devices, electronic medical records, and other patient data, these may well become the safest and quickest mechanisms for tapping real-world data for better research into ailments with sizeable patient populations. Researchers may achieve greater patient populations which are homogenous and get vital insights alongside. Here are some other points worth noting:  The outcomes of clinical trials are major metrics with regard to performance, at least as far as companies and investors are concerned. They are also the beginning of collaborations between patients, groups, and the healthcare sector at large. Hence, there is a clearly defined need for big data analysis in clinical trials as evident through the above-mentioned aspects.  FAQs How can data analytics be used in clinical trial design and analysis? Data analytics can be readily used for clinical trial design and analysis, expanding patient selection criteria, swiftly sifting through various parameters and helping researchers better target matching patients who match the criteria for exclusion and inclusion. Data analysis methods also enable better conclusions from data while also improving clinical trial design due to better visibility of the possible/predicted risk-reward outcomes.  What are the benefits of using data analytics in clinical trial design and analysis? The advantages of using data analytics in clinical trial design and analysis include the integration of data across diverse sources, inclusive of third parties. Researchers get more flexibility in terms of research, finding it easier to analyze clinical information. Predictive analytics and other tools are enabling swifter disease detection and superior monitoring.  What are the challenges of using data analytics in clinical trial design and analysis? There are several challenges in using data analytics for the analysis and design of clinical trials. These include the unavailability of skilled and experienced resources to implement big data analytics technologies, data integration issues, the uncertainty of the management process, storage and quick retrieval aspects, confidentiality and privacy aspects and the absence of suitable data governance processes.  What are the best practices for implementing data analytics in clinical trial design and analysis? There are numerous best practices for the implementation of data analytics for the analysis and design of clinical trials. These include good clinical data management practices, clinical practices, data governance

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