Tag: Indus net technology

Blockchain beyond cryptocurrency

Blockchain beyond cryptocurrency

May 25, 2023

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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Ethereum Virtual Machine (EVM) & Smart Contract

Ethereum Virtual Machine (EVM) & Smart Contract

May 19, 2023

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

May 18, 2023

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 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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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

May 2, 2023

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 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 rise of insurtech startups and the implications for traditional insurance companies

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

April 27, 2023

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

April 26, 2023

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 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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Digital Asset Management - SharePoint Syntex to the Rescue

Hackathon Diaries #6-Digital Asset Management

April 20, 2023

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

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