Tag: Data Analytics

Rise of parametric insurance

The Rise of Parametric Insurance: Paying Out Based on Data, Not Damage

August 27, 2024

Use cases for data analytics in insurance have evolved considerably in recent years. One of the biggest such innovations is parametric insurance that is steadily rising in importance, with the increasing unpredictability of the world and environment today. Instech reports have already highlighted how parametric solutions saw record growth in 2022 and the trend is set to continue over this decade as well. What is Parametric Insurance? Often labeled as climate risk insurance or disaster insurance, parametric insurance can be classified as the following: Why Parametric Insurance is Beneficial? Insurtech players have already started realising the value of parametric insurance solutions, particularly for helping communities build financial resilience in the light of unpredictable and volatile climate risks. Some of the key benefits include the following: Are There Any Disadvantages? While the advantages of parametric insurance clearly outweigh the disadvantages, there are still a few moot points in this case. How It All Stacks Up Parametric insurance is an innovative product that is well-positioned to take-off in the current global scenario, despite its potential drawbacks. Technology will play a vital role in data-based payouts in the future. This will include real-time tracking from ground-based sensors and satellite/radar imagery and data. The aim of the insurance product in this case is to minimise risks as much as possible based on advanced data and technological prowess. In fact, it can well transform into a more effective and robust climate risk insurance model, thereby incorporating the costs of climate change in a better way. With the certainty of these products, a higher portion of premiums that buyers spend on coverage come back to them as claims, instead of being spent through disputes and frictional expenditure. Parametric insurance is also customisable to various industries and corporate clients, since it is not concerned about the type of asset, rather wider financial losses due to triggering events. Clients in the corporate sector can thus tackle the bigger financial impact on operations in case of climate events, including vendors, suppliers, customers, and logistics. IoT and other new technologies will keep powering parametric solutions, with warning systems that can inform customers about potential risks. This will help them take preventive measures in advance. LLMs or large language models are also positively impacting the sector and will completely change underwriting in the future. They will process vast datasets swiftly, including claims history and historical occurrences, thereby enabling underwriters to predict future claims more efficiently. So, to sign off, it can be said that parametric insurance should be around in the coming decade and even beyond. With more insurance companies, underwriters, customers, and brokers recognising it as a good solution for risk transfer, there will be more evolution into secondary complexities and events like floods, hail, thunderstorms, wildfires, and more. With data being leveraged comprehensively for pricing, this form of insurance will make underwriting easier and lower the time to quote and finalise policies or even settle claims. This will help insurance companies save more time and money, which will prove hugely beneficial in the long run. FAQs 1. What is Parametric Insurance and How Does It Differ from Traditional Insurance? Parametric insurance is a form of insurance where payouts are triggered based on certain pre-determined thresholds/parameters. It is different from traditional insurance, since it is not concerned with the actual loss/damage. 2. How Does Parametric Insurance Work? In case of any event/disaster that triggers any specific thresholds/parameters like wind speed, earthquakes of a certain magnitude, and so on, pre-fixed payouts will be given to policyholders, irrespective of their actual losses and whether they have suffered losses or not. Claims processing will be near-automatic due to pre-fixed agreements. 3. What Are the Benefits of Parametric Insurance for Policyholders? Communities and people without access to conventional insurance can benefit from higher financial resilience against climate change with parametric insurance. It enables swift payouts and more agile operations, while saving time and resources. It also lowers the chances of disputes with policyholders and fraudulent claims. 4. What Types of Events Are Covered by Parametric Insurance Policies? Parametric insurance policies can cover a wide range of events including earthquakes, poor crop yields/harvests, natural disasters like hurricanes and cyclones, and so on. 5. How is the Payout Determined in Parametric Insurance? The payouts in parametric insurance are determined based on whether specific thresholds have been triggered in the case of any event. There is no consideration for the actual loss of the policyholder. If these parameters are triggered, then payouts are released to policyholders and are pre-fixed amounts. These amounts are estimated by policyholders in most cases, depending on their calculations of the potential financial cost of damages due to varied natural disasters and other such events.

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Data Analytics in Health Insurance for Better Risk Management

2024 Trends: Data Analytics in Health Insurance for Better Risk Management

January 8, 2024

The health insurance industry has witnessed a rapid evolution in terms of digitisation, which has also swiftly transformed several other sectors worldwide. One of the biggest contemporary trends in health insurance (and poised to last for the foreseeable future) is the harnessing of data analytics to ensure superlative risk management among other benefits. Pharmacy managers, insurance companies, healthcare providers, and other industry stakeholders are already leveraging analytics to tackle issues of fast-growing healthcare costs. Insurance companies are already using them to identify high-risk patients and operate accordingly. Why Data Analytics is Indispensable From an operational standpoint, data analytics is indispensable for health insurance companies. They keep generating huge data volumes internally through sales and engagement but also get sizable chunks from varied external sources. When this data spreads out through multiple systems, it becomes an uphill task for insurers to effectively use and track the same. This is why analytics-based solutions that can fuse and consolidate data from multiple touch points and sources have gained relevance in recent times. A centralised data gathering system with consistent analytics and actionable insights is thus one of the key trends in health insurance currently. How Data Analytics Enables Better Risk Management Data analytics is undoubtedly contributing towards enabling insurers to manage their risks better. Here are some aspects that should be highlighted in this regard. These benefits have gained even more traction in recent years if one considers how insurers lose a whopping $40 billion per annum on account of fraudulent claims as per Gartner reports. Many insurance companies also estimate 10-20% of claims to be fraudulent while identifying less than 20% of the same. Suspicious and fraudulent behaviour and patterns can be identified with data analytics, with insurers creating diverse models to enable swift detection based on historical data and activities. For instance, analytics has reportedly helped the Czech Republic’s Allianz Insurance to save a whopping US$4.5 million annually by lowering fraudulent claims paid by it. This is just one of the instances that testify to the need for insurance companies to rapidly adopt data analytics into their operational frameworks. Analytics can be used to conduct risk evaluation in real-time which will help organisations respond swiftly in volatile scenarios. For example, in the case of auto insurance, an accurate assessment of risks posed by specific drivers will help insurers create more competitive premiums. Cars connected to the internet will help them gather large volumes of data accordingly. Insurance firms can now predict the chances of drivers being involved in accidents by analysing driving habits and behavioural data. Some other advantages of data analytics include easier customer lifetime value (CLV) prediction and prospective claim forecasting. FAQs How can data analytics contribute to more precise risk assessment in the health insurance industry? Data analytics can enable more accurate risk assessments in the health insurance sector. It can help identify fraudulent behavioural patterns and flag the same for review before the actual payout. What impact do 2024 trends in health insurance data analytics have on customer experiences and personalised offerings? 2024 trends in health insurance data analytics will have a positive impact on customer experiences. Insurers will be able to personalise their offerings for customers based on their behavioural data and also offer custom premiums with incentives for recommended and healthier actions.

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The Impact of AI and Data Analytics in Pharma Research

The Impact of AI and Data Analytics in Pharma Research

January 3, 2024

AI in pharma research has the potential to be a veritable game-changer for the entire sector. Data analytics in pharmaceuticals along with other innovations like data-driven research and AI/machine learning in pharma have made it comparatively easier to develop new drugs and tackle emerging diseases. Biopharma research remains expensive and lengthy although AI can play a vital role in enabling higher probabilities of success and boosting productivity.  How AI and Data Analytics are Indispensable for Pharma Research Here are a few ways in which AI in pharma research can be indispensable for the industry soon. AI in pharma research will enable the creation of feedback loops for further refining the predictive abilities and stability of AI algorithms. They will also inform experimental design functions accordingly. Through analytics and data science tools, pharma can capture the entire value of the present portfolio and create mechanisms and IP for driving research in the future. AI-drug discovery is already taking place with several companies building their pipelines. Biopharma entities are also developing top-down and executive strategies where AI-backed discovery can be a vital indicator and enabler of performance in the future. Automated image analysis or lead optimisation will be bolstered along with the collection of experimental data in a reusable manner, automated screening algorithms linking molecular descriptions with hits or desired outputs, blueprinting, enabling better testing and learning solutions for product delivery and designing new screening protocols. AI is already transforming the research space through the application of machine learning and data science to huge data sets, enabling swifter discoveries of newer molecules. It enables cross-referencing of published scientific literature with alternate sources of data (clinical trial data, conference abstracts, public databases, and unpublished data) to surface therapies that are promising. Medicines can be delivered in months at times instead of several years as a result. AI can also help lower clinical trial costs and cycle times while enhancing overall clinical development outcomes considerably. ML and AI are already being used for automatically generating study protocols while NLP (natural language processing) is being used to scale up manual tasks. AI algorithms can also ensure continual clinical data cleaning, coding, aggregation, management, and storage. Through automation and centralisation of intakes for adverse event reports backed by AI-backed technologies like NLP and OCR (optical character recognition), case documentation workloads are considerably reduced for expediting investigative processes. These are only a few of the widespread benefits that data analytics, AI, and ML can bring to the table for life sciences and pharmaceutical companies, especially in terms of research and development. FAQs What role will AI play in optimising clinical trials and research methodologies, and how is this expected to impact the pharmaceutical industry in 2024? AI will play a huge role in the optimisation of research methodologies and clinical trials in the future. This will have a positive impact on the pharmaceutical industry in 2024 and beyond since AI will optimise patient recruitment, predict the efficacy of treatments, automate data analysis, and boost safety tracking. It will also accelerate trial procedures while lowering costs and enhancing data quality. This will lead to more personalised and successful clinical trials. How will integrating AI and data analytics accelerate drug discovery processes within the pharmaceutical industry in the upcoming year? Drug discovery processes within the pharmaceutical industry can be accelerated in the upcoming year through the integration of data analytics and AI. This will be possible through the prediction of drug-target interactions, evaluation of the safety and efficacy of drugs that are repurposed, and identification of newer options for treatments. Potential biomarkers can be identified while researchers can easily analyse big data sets and design new molecules while forecasting the efficacy levels of potential drug candidates accordingly.

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Enhancing Underwriting Precision: The Role of Data Analytics in Insurance Broker Decision-making

Enhancing Underwriting Precision: The Role of Data Analytics in Insurance Broker Decision-making

December 28, 2023

Data analytics in insurance has been a veritable game-changer for the industry in recent times. It has become a major solution for several issues while enabling a more personalised experience for customers. Insurance brokers are steadily embracing data-driven insurance intending to bolster their decision-making process. Let us look at some of the biggest advantages of deploying analytics in the insurance sector. Benefits of Data Analytics in Insurance for Brokers Insurance broker decision-making can improve considerably with the deployment of data analytics. Here are some of the biggest advantages worth noting in this regard. These are some of the core benefits that can boost insurance broker decision-making greatly. Analytics can transform the operational side of the business while freeing up brokers to focus on strategising for future growth without worrying about the operational and administrative aspects of the sector. FAQs What is the role of data analytics in enhancing underwriting precision for insurance brokers? Data analytics can greatly enhance underwriting in terms of accuracy for insurance brokers. It can estimate risks better and predict premiums more accurately for customers who are more prone to the same. In what ways can insurance brokers leverage data analytics to tailor insurance solutions for individual clients? Insurance brokers can seamlessly leverage data analytics for tailoring insurance solutions for their clients.  Data-driven insights will enable better customer understanding and an idea of preferences. This will help insurance brokers come up with genuine recommendations and more personalised products/services that will help customers meet their needs better. What types of data sources are most valuable for insurance brokers seeking to enhance underwriting precision through analytics? Insurance brokers who are looking to enhance their underwriting processes through analytics rely on varied data sources. Some of these sources include social media platforms, demographics, lifestyle, age, medical data, and more.

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2024 Trends: Data Analytics in Health Insurance for Better Risk Management

2024 Trends: Data Analytics in Health Insurance for Better Risk Management

December 20, 2023

The health insurance industry has witnessed a rapid evolution in terms of digitisation, which has also swiftly transformed several other sectors worldwide. One of the biggest contemporary trends in health insurance (and poised to last for the foreseeable future) is the harnessing of data analytics to ensure superlative risk management among other benefits. Pharmacy managers, insurance companies, healthcare providers, and other industry stakeholders are already leveraging analytics to tackle issues of fast-growing healthcare costs. Insurance companies are already using them to identify high-risk patients and operate accordingly.  Why Data Analytics is Indispensable From an operational standpoint, data analytics is indispensable for health insurance companies. They keep generating huge data volumes internally through sales and engagement but also get sizable chunks from varied external sources. When this data spreads out through multiple systems, it becomes an uphill task for insurers to effectively use and track the same.  This is why analytics-based solutions that can fuse and consolidate data from multiple touch points and sources have gained relevance in recent times. A centralised data gathering system with consistent analytics and actionable insights is thus one of the key trends in health insurance currently.  How Data Analytics Enables Better Risk Management Data analytics is undoubtedly contributing towards enabling insurers to manage their risks better. Here are some aspects that should be highlighted in this regard.  These benefits have gained even more traction in recent years if one considers how insurers lose a whopping $40 billion per annum on account of fraudulent claims as per Gartner reports. Many insurance companies also estimate 10-20% of claims to be fraudulent while identifying less than 20% of the same.  Suspicious and fraudulent behaviour and patterns can be identified with data analytics, with insurers creating diverse models to enable swift detection based on historical data and activities. For instance, analytics has reportedly helped the Czech Republic’s Allianz Insurance to save a whopping US$4.5 million annually by lowering fraudulent claims paid by it. This is just one of the instances that testify to the need for insurance companies to rapidly adopt data analytics into their operational frameworks.  Analytics can be used to conduct risk evaluation in real-time which will help organisations respond swiftly in volatile scenarios. For example, in the case of auto insurance, an accurate assessment of risks posed by specific drivers will help insurers create more competitive premiums. Cars connected to the internet will help them gather large volumes of data accordingly. Insurance firms can now predict the chances of drivers being involved in accidents by analysing driving habits and behavioural data. Some other advantages of data analytics include easier customer lifetime value (CLV) prediction and prospective claim forecasting.  FAQs How can data analytics contribute to more precise risk assessment in the health insurance industry?  Data analytics can enable more accurate risk assessments in the health insurance sector. It can help identify fraudulent behavioural patterns and flag the same for review before the actual payout.  What impact do 2024 trends in health insurance data analytics have on customer experiences and personalised offerings? 2024 trends in health insurance data analytics will have a positive impact on customer experiences. Insurers will be able to personalise their offerings for customers based on their behavioural data and also offer custom premiums with incentives for recommended and healthier actions. 

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2024 Outlook: Data-Driven Transformations in the Indian Insurance Brokerage Industry

2024 Outlook: Data-Driven Transformations in the Indian Insurance Brokerage Industry

December 15, 2023

The Indian insurance brokerage ecosystem has witnessed rapid change in recent years, driven by data-driven transformations. This has revamped overall decision-making, marketing, and customer support in a major way and the trend looks set to continue in the future. Here is a closer look at how data-based transformation will ultimately impact the operations of brokerages in the Indian insurance market in 2024 and beyond.  Ways in Which Insurance Brokers Will Leverage Data Data analytics in insurance will be a major game-changer shortly for brokers. Indian insurance brokerages will ultimately turn to these models of operation.  Brokerages will also embrace technology to offer customers better services across WhatsApp, emails, SMS, or phone among other channels. They can reach out better to customers at the right time and place along with forecasting buying trends and patterns. Data analytics will help with customer behavior analysis which will unearth several invaluable insights in turn for brokers. The end goal will be to provide customised and need-based solutions across segments. Target consumers can be easily identified and segmented while products can be tailored to meet their needs. Brokerages can also leverage technology to help insurers with better underwriting, eliminating fraud enabling risk management, and maximising usage of alternate data sources. They can also use data for collaborations with other parties for specific use cases and scenarios.  These are some of how Indian insurance brokerages will gradually adopt data-driven transformations to help build competitive advantages throughout the insurance landscape.  FAQs What key data-driven transformations are anticipated for the Indian insurance brokerage industry in 2024?  The Indian insurance brokerage industry in 2024 could adopt several data-driven transformations including personalised customer products and services along with automated and faster claim settlements and processing. They can also delve into personalised pricing decisions and models to benefit their customers.  What role will emerging technologies play in shaping the data-driven landscape of Indian insurance brokerages in 2024?  Several emerging technologies like AI, automation, machine learning, and data analytics will have a vital role to play in shaping the entire data-based Indian insurance brokerage landscape in 2024. They will make it easier to build customer profiles, target and segment customers in specific categories, predict customer buying patterns based on behavioural habits and preferences, and customise products/services and pricing accordingly among many other use cases.  In what ways will data analytics impact risk management and decision-making processes for Indian insurance brokers in 2024? Data analytics will help Indian insurance brokers make better decisions and manage risks more effectively in 2024 and beyond. Analytics will help them identify potential risks at the customer’s end and also eliminate the chances of fraud. At the same time, it will help make better decisions on personalising pricing as per customer habits and lifestyle preferences. It will also help make better decisions on policy issuance depending on customer data from multiple channels. 

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How Indian BFSI Firms are Thriving through Data-Driven Strategies

How Indian BFSI Firms are Thriving through Data-Driven Strategies

December 13, 2023

BFSI firms in India are innovatively leveraging data-driven strategies to thrive and flourish in recent times. Online banking has already generated customer expectations regarding cutting-edge services irrespective of location and time. Open banking and embedded finance have also raised the bar further, enabling customers to get credits through non-bank enterprises. Open banking is also enabling third-party access through APIs to financial information. With the increase in advanced banking operations, customers are steadily expecting their institutions to anticipate their needs better.  At the same time, another indicator for BFSI firms about using data analytics in BFSI is to enable better customer experiences for future growth. A Salesforce report in 2019 covered 8,000 business customers and buyers globally and reported how 84% of customers feel that customer experiences are as crucial as the services and products offered by any financial institution. Data shared across multiple touch points and channels have thus opened up several new opportunities for BFSI players throughout the Indian finance sector to flourish amidst a competitive landscape.  How Data-Driven Strategies are Helping BFSI Firms Flourish  Data analytics in BFSI and other data-driven strategies are enabling BFSI firms in India to thrive and grow in the present scenario. Here are some pointers worth noting in this regard.  It is a fast-changing world that necessitates the usage of data-driven strategies across the board for BFSI firms. The digital banking platform segment is already expected to grow by a whopping 11.2% (CAGR or compounded annual growth rate) from 2021 to the year 2026. Bots are leveraging data to provide better customer service across touchpoints without requiring branch visits or conversations with agents. They can service customer requests easily while handling other activities seamlessly.  Conversational AI platforms are also using NLP that is integrated with IVR systems. These systems can take calls by answering repetitive questions and prevent any customer panic. Customers are assisted in swiftly resolving queries while calls that are complex are transferred to agents. Banks are offering branch-like services with data-driven strategies, building customer profiles/personas, predicting behaviour, and recommending ideal financial services and products.   Fraud detection and security models are trained on continual incoming data, helping BFSI firms know more about normalised activity levels, transaction anomalies, deviations, and more. Another method is behavior profiling which studies customer data and accounts to build profiles and understand where/what kind of transactions have taken place. Prescriptive analytics also helps leverage the data that is gathered by predictive analytics to recommend the measures to be taken once fraud is identified. These are some of the many ways in which data-driven approaches are helping BFSI players thrive in an increasingly competitive Indian finance sector. As they say worldwide, data is the new oil and it will soon be the differentiator and competitive advantage that companies in every sector will want to harness, banking and financial services included.  FAQs What key benefits do Indian BFSI firms experience through the adoption of data-driven approaches?  Data-driven approaches are helping BFSI firms in India obtain several major benefits including the ability to personalise products/services for customers, identify and eliminate fraud, predict risks and manage them accordingly, and a lot more.  In what ways are data-driven strategies enhancing decision-making within the Indian BFSI sector? Data-driven strategies are boosting overall decision-making within the Indian BFSI sector. Banks and financial institutions are leveraging data to make better decisions on granting loans or other products, offering personalised services or solutions to customers, identifying and mitigating risks, and so on. 

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Data-Driven Decision Making: How Advanced Analytics Is Shaping Fintech Strategies

Data-Driven Decision Making: How Advanced Analytics Is Shaping Fintech Strategies

November 21, 2023

Data-driven decision-making and better fintech strategies are a result of advanced analytics in fintech, a trend which is making the whole sector sit up and take notice of their immense potential. Open banking and big data analytics are shaping the financial sector as it prepares for a more customer-centric and digital shift in the near future.  How has Data Analytics in Finance Been a Game-Changer for the Industry? Advanced analytics in fintech has completely changed the operational rules of the game for these platforms along with other financial institutions at large. Customers now have more control over their finances with open banking and expect more personalised experiences as a result. Big data analytics in finance is forecasted to continue its growth momentum, leading to newer fintech innovation opportunities. More platforms and market players will look at leveraging big data to deliver better services to customers along with tailored and personalised products and experiences.  Here’s how advanced analytics in fintech can help industry stakeholders in the current scenario:  As can be seen, advanced analytics in fintech has several potential benefits that will usher in a whole new era of smart banking and finance solutions in the future. Companies can easily optimise customer acquisition with data-driven marketing and personalisation. They can also scale up customer retention as a result, while identifying better opportunities for up-selling or cross-selling along with communicating better with customers in a personalised manner. They can also combat cyber-security issues and fraud better through machine learning algorithms that identify unusual patterns, anomalies, and other suspicious activities. AI and automation can be used to swiftly gather insights from vast amounts of information while also enabling automated customer service and communication via Chatbots.  Sounds interesting? Analytics and AI are poised to bring in a whole new world for customers and fintech players alike. The best part is that there are only upsides for all stakeholders in the process.  FAQs How is advanced analytics revolutionising data-driven decision-making in the fintech industry? Advanced analytics is helping fintech players make data-driven decisions related to personalised customer communication, marketing, offering tailored products and services, meeting customer demand, and also in terms of evaluating market conditions and responding to them more accurately.  What types of data sources and analytics tools are fintech companies leveraging to gain a competitive edge? Fintech companies are leveraging various data sources including their own databases, online channels and social media platforms, POS transactions and other transaction histories, and more. They are also leveraging AI and machine learning along with automation and big data analytics to gain a competitive edge in their respective market segments.  How can data-driven insights lead to more personalised fintech products and services for customers? Data–driven insights help fintech companies build personalised customer profiles and offer customised products and services to customers based on their transaction history, behavioural habits, preferences, and other parameters.  What are the key challenges and considerations when implementing advanced analytics in fintech strategy development? Some of the major considerations or challenges while implementing advanced analytics in fintech strategy development include regulatory norms, customer consent and data privacy, and the safety of customer data.

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Top Data Analytics Trends in Pharma to Look Out For in 2024

Top Data Analytics Trends in Pharma to Look Out For in 2024

November 14, 2023

There are numerous data analytics trends in pharma that have disrupted the sector steadily in recent years. The industry itself is seeing a major integration of things like blockchain, Industry 4.0 techniques, and AI (artificial intelligence) among other game-changers.  Along with pharma data analysis and the usage of real-world data for collecting patient experiences, blockchain for secure transactions, and even managing patient records, there are several use cases worth considering in this regard. There is also a steady emphasis on offering augmented, virtual, and mixed-reality solutions throughout the industry spectrum. Here are a few pharmaceutical industry trends worth noting from a data analytics standpoint.  Pharma Analytics 2024 Trends  Here are some of the top data analytics trends in pharma that are worth noting.  These are some of the top data analytics trends in pharma that deserve to be noted in the current scenario. Data analysis and insights are completely changing the game for pharmaceutical companies in terms of enabling benefits throughout the entire spectrum.  FAQs How is the utilization of big data and advanced analytics improving drug discovery and development processes? Big data analytics works to reduce the costs and time of clinical trials. Through the usage of machine learning (ML) algorithms, pharmaceutical companies can easily identify sub-groups of patients which are more likely to respond to specific treatments. Researchers can also design more targeted and smaller trials that will succeed more. Data sets can be integrated with big data from diverse sources. Through this analysis, researchers can easily identify drug indicators, newer targets, and drug response biomarkers with lower risks.  What role does artificial intelligence play in optimizing pharmaceutical research and manufacturing operations? AI-based algorithms may optimize and analyze drug candidates by taking several aspects into account. These include pharmacokinetics, safety, and efficacy levels. It enables researchers to fine-tune specific therapeutic molecules to boost overall effectiveness while lowering side effects simultaneously. Predictive maintenance is also used through artificial intelligence (AI) throughout the manufacturing process. It may be applied to production data for enhancing maintenance planning and the prediction of failures.  What challenges and opportunities are associated with data analytics in pharmaceuticals, and how can companies stay competitive in this evolving landscape? Data engineering and analysis come with various challenges including the management of data from diverse sources while also sticking to stringent regulatory requirements and safeguarding the privacy levels of patients. There are varied challenges relating to data quality along with data silos, governance, and integration. These can be overcome through master data management platforms which ensure more reliable and accurate data that helps companies build their competitive advantages accordingly. 

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Analytics-Driven Personalisation: Redefining the Customer Experience in Banking

Analytics-Driven Personalisation: Redefining the Customer Experience in Banking

October 16, 2023

Analytics-driven personalisation is the biggest recent trend that has completely changed the game in terms of enabling personalised banking along with improved customer experience in banking. Digital transactions, payments, and banking platforms have completely changed the modus operandi as far as both customers and executives are concerned. At the same time, the higher digital engagement and transaction volumes lead to the generation of huge amounts of data on a daily basis. This is in the form of both non-transactional and transactional information.  Banks are now finding several merits in tapping and analysing this data to gain invaluable insights for positively transforming customer experiences and processes. Technologies like banking analytics are being used in tandem with machine learning, artificial intelligence, and big data analytics to generate the best possible results for banks in this context. Even McKinsey Global has stated how data-driven entities are 23 times likelier to acquire new customers, while being six times likelier to retain them and 19 times as likely to be profitable due to this aspect.  Another key aspect lies in the fact that banking analytics or data analytics in this segment had a value of approximately $4.93 billion in 2021 and is estimated to hit $28.11 billion within 2031 (indicating compounded annual growth rates or CAGR of 19.4%). There are several data or touch points for customers including websites, mobile apps, digital transactions, social media platforms and a lot more. Rich data can be used for redefining customer experiences while also predicting customer engagement and mapping the journey.  How Analytics-Driven Personalisation is the Key Factor When it comes to offering personalised banking and redefining customer experiences, big-data analytics is the key element that institutions are looking to leverage in the current scenario. Here are some pointers worth noting in this regard.  Several banks and financial institutions have multiple products for customers which cater to varying requirements. Redefining customer experiences thus becomes a major differentiator for these financial institutions in order to enhance customer satisfaction and retention levels alike. Gaining a better understanding of customers and identifying gaps or potential issues will also help improve the overall experience for customers while enabling more personalisation at the same time with full scalability.  What are the challenges of data analytics in banking?  There are a few challenges of leveraging banking analytics that institutions also need to be aware of. These include:  However, analytics-driven personalisation is the biggest trend that will completely reshape customer experiences across banks and financial institutions. Customers now engage across several touchpoints and expect more personalised banking solutions and quick assistance and support for their queries. Hence, institutions will have to rely more on data analysis and insights to make better decisions that lead to improved customer experiences and higher retention. However, maintaining a customer-centric approach is the biggest takeaway that banks should keep at the forefront while scaling up data analytics initiatives simultaneously.  FAQs Analytics-driven personalisation greatly enhances the banking experience for any customer. Banks get a full view of the customer profile and specific needs, pain points and requirements. Hence, they can customise their offerings and solutions to meet these needs while solving the pain points and making sure that the customer gets the right solutions at the right time.  Both transactional and non-transactional data are used for driving analytics-driven personalisation in banking. This includes data directly gathered from transactions across multiple channels and also other data from surveys, forms, websites, mobile applications, social media platforms and many other sources.  There are a few considerations and challenges that banks should keep in mind while implementing personalisation through analytics. Data quality and integrity should be a major focus area, since poor quality may completely jeopardise the whole process. Other considerations include data silos, gathering disparate data across systems, integration and dealing with legacy infrastructure.  With more personalised services and engagement, customer experiences naturally improve over time. This leads to higher loyalty and superior engagement since customers get solutions tailored to their needs and their pain points are addressed by banks swiftly due to analytics-driven insights.

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