The Value of Data in Open Banking

The Value of Data in Open Banking

Open banking is one of the key drivers of the financial revolution today, bringing in higher competition and innovation in the banking sector like never before. Open banking is the practice of securely sharing a customer’s financial data – with consent – between the bank and authorized third parties (including enterprises that may not be active within the financial sector at present). This exchange of data, enabled by Application Programming Interfaces (APIs), makes it easier for new players to offer a larger variety of services giving customers more choices and better control over their financial data.

Open banking has been linked to the Second Payment Services Directive (PSD2) in 2018 in Europe. PSD2 allows financial firms to sell services by categorizing them into two buckets namely, Account Information Service Provider (AISP) or Payment Initiation Service Provider (PISP). AISP certified financial firms can access and view account data via an API with their bank, while a PISP certification allows a bank to make payments on behalf of its customers.

Why does open banking matter?
Customers are hungry for change and are unhappy with their bank’s payments and banking capabilities. Open banking can help set a bank on track for success and present opportunities by putting the customer at the heart of every decision. Instead of being seen as a threat that increases competition, the focus should be on the huge revenue potential that open banking can unleash. Insider Intelligence estimates that in the UK alone, open banking can enable small and medium-sized businesses (SMBs) to reach USD 2 billion by 2024.

Banks can push their APIs beyond regulatory requirements to offer existing customers new services and export data to personal finance managers or small business accounting apps. They can also sell specialized services, such as consumer credit check services to fintechs or identity management tools to smaller banks. This will allow them to engage third-party financial firms to build innovative customer offerings across different avenues.

Incumbent banks fighting about the unfairness of open banking need to understand that all stakeholders including banks, fintechs, third-party aggregators, and regulators, can share their learning and grow the market faster. Instead of defending ownership of data and tools, they should adopt API-driven open banking initiatives for a definite rise in revenue.

Customers, meanwhile, will have better options to decide which financial products they need and will be able to choose products that suit their real needs. And thanks to APIs, customers can aggregate data from multiple accounts, cards and banking products of different entities together in a single app, and manage their finances with greater transparency.

The very valuable banking data
A customer’s transactional data is the most important asset for traditional banks. Banks can leverage data to transform the customer experience and generate new and personalized offers. But this data is locked away in legacy mainframes and applications and is not easily accessible, creating a level of complexity and cost that slows time to market and prevents the implementation of next gen offerings.

By setting up the rules of engagement with PSD2, the EU has changed the game. Advanced analytics can enhance the delivery of financial services to both retail consumers and business customers. Personalised experiences and products powered by AI and advanced analytics are key to improving customer experience, product development, credit assessment and operational performance. Data can also serve as a catalyst for new financial management and business models. Unfortunately, in case of several banks the use of data is patchy and inconsistent due to which the outcomes are underwhelming and without any significant improvement.

Open banking Needs Good Data Analysis
Banks thus need a data architecture that’s agile, scalable, robust and easy to use. Fully understood, this data can reveal what customers are doing with other banks, uncover gaps in service models, point to new competitive threats and suggest appropriate customer strategies. As this data is complex and high in volume, it requires both good analytics as well as top notch data engineering skills to transform the data into actionable insights. Without this capability, banks are missing out on invaluable contextual customer insights that can turn open banking into a competitive advantage.

To be effective, open banking needs stable, coherent, non-federated and organized data. Instead of a data swamp, banks need a digital-first and data-centric approach that will allow them to scale their business and better serve customers. Good data analytics will also help banks better understand the financial environment and make smarter decisions.

Using Data for Deep Customer Focus
As financial offerings become more digital and commoditized, standing out in the crowd requires moving from a product-centric focus to a more customer-centric experience. AI, machine learning, and big data are enabling more personalized customer experiences, allowing fintechs to wow customers and siphon them away from traditional financial providers.

Fintechs have been quick to use technology to become agile and adapt quickly to changing market conditions. They use algorithms to process the vast amount of data that is generated every day, create actionable items and predict and anticipate customer behavior. They can share potential products, upsells and cross-sells with customers. As a result, rethinking customer interaction has become a part of product development whereas only some decades ago the perspective on the product itself dominated the development processes. Thus, banks should aim to provide customers a tailored experience by adapting their digital structure. To make this possible, data will be needed on who the customers are and what they really want. Technologies such as big data as well as distinguished algorithms are potential tools that can help to gain these insights.

Despite initial reservations and scepticism, banks are beginning to see the benefits of open banking, and how it can improve customer experience and help incumbent banks become more agile. Open banking, we believe, is all about a data-driven continuous improvement in customer centricity and leads to increased financial integration with value-added services and overall improvements. Banks are already sitting on a wealth of data, and all they need are the right tools and partners to unlock the potential of that data.

Generative Design – The Power of Cloud Computing and Machine Learning to redefine Engineering

Generative Design – The Power of Cloud Computing and Machine Learning to redefine Engineering

Imagine a technology, that helped Airbus shave off 45% (30kg) of the weight of an interior partition in the A320. That weight decrease resulted in a massive reduction of jet fuel consumption and several thousand tons of carbon dioxide emission when applied across its fleet of planes. It equaled taking 96,000 passenger cars off the road for a year. The technology in question is Generative Design.

So, what is Generative Design? Basically, by using artificial intelligence (AI) software and the computing power of the cloud, the generative design enables engineers to create thousands of design options by simply defining their design problem and then inputting basic parameters such as height, load-bearing capacity required strength and material options. It, therefore, replicates the natural world’s evolutionary approach with cloud computing to provide thousands of solutions to one engineering problem.
With generative design, engineers are no longer limited by their own imagination or past experience. Instead, they can collaborate with technology to create smarter, more cost-effective and environment-friendly options.

Since generative design can handle a level of complexity that is impossible for human engineers to conceive of, it can also consolidate parts. Thus, single parts can be created that replace assemblies of 2, 3, 5, 10, 20, or even more separate parts. Consolidating parts simplifies supply chains, maintenance and thereby reduces overall manufacturing costs. With its ability to explore thousands of valid design solutions, built-in simulation, awareness of manufacturability and part consolidation; generative design impacts far more than just design. It impacts the entire manufacturing process. Thus, the generative design delivers a quantum leap in real-world benefits. It can lead to massive reductions in cost, development time, material consumption and product weight.

As AI becomes a part of all work processes, generative design can become the norm for product design. The order of the day will be products that are better suited to consumer needs, and are manufactured in less time; with less material waste, less fuel waste, and less negative impact on our planet.
However, deploying an algorithm-based design will lead to a paradigm shift in engineering because as Franck Mouriaux, a global expert in aerospace engineering, once stated “Engineers were not trained to formulate the problem. They were trained to find solutions.” But it is formulating the problem, which is the key to generating good geometry.
No doubt, engineers can express a design challenge in natural language. For example: If a suspension part of a car, was to be made significantly lighter, will the car still be secure when traveling at a certain speed? However, formulating such a problem in computable terms – regions targeted for material reduction, regions that must remain unchanged for safety and aesthetics, anticipated stress loads on the part while the object is moving, the direction of the loads, the type of vibrations it is likely to endure, and so on – is a still challenging. The skill to express the design problem as a set of parameters can be found in simulation software users. But this requirement can prove a steep learning curve for people trained in CAD.

Generative design inquiries are usually not typical yes/no questions (will it break or will it hold?); they’re formulated as what questions (under these conditions, what are the best suspension design options for a safe and secure car?). As you add additional constraints or parameters (such as acceptable weight range for the part, preferred manufacturing materials and more), the design options change.

Quite often, what is mathematically optimal – geometry with sufficient material reinforcement to counter the anticipated stress in different regions – is impractical to manufacture or produce, either due to cost concerns or the limitations of the production methods available. Additive manufacturing (AM) now gives the option to 3D print certain complex geometric forms that cannot be machined; however, even with AM, certain limitations persist.
The onset of practical artificial intelligence algorithms has enabled the possibility of mainstream generative design tools. That means engineers can create thousands of design options inherent to their digital design and choose which design meets their needs to the fullest. They can then solve manufacturing constraints and ultimately build better products.

Therefore, many companies are embarking on this path, as they have realised that it is a powerful addition to an engineer’s design arsenal. It results in better ideas and products that are lighter and accomplish their directives better. And so, it comes as no surprise that we see applications beyond the aerospace industry, highlighted by the following use cases:

  1. In the automotive industry, General Motors was one of the first companies to use generative design to reduce the weight of its vehicles. In 2018, the company worked with Autodesk engineers to create 150 new design ideas for a seat bracket and chose a final design that proved 40% lighter and 20% stronger than the original component.
  2. Under Armour has used generative design algorithms to create a shoe with the ideal mix of flexibility and stability for all types of athletic training. It was inspired by the roots of trees. The algorithm came up with a rather unconventional geometry. The prototypes were then 3D printed into a shoe and could be tested by more than 80 athletes in a fraction of the time than it would have taken in the past.

Simply put, Generative design is a tool that uses machine learning to mimic a design approach similar to nature. It interfaces with engineers by allowing them to input design parameters to problem-solving. Thus, the engineer can decide to maintain certain material thicknesses, despite higher costs, due to the higher load-bearing capacity required. All this can be fed into generative design tools.

The algorithms provide generative designs that meet the input criteria. The role of the engineer is then to pick the most suitable design and modify it. In essence, it leads to a digital shortcut to optimizing the perfect design. Thus, it basically kickstarts the entire design process.

In order to build any item, instead of starting with some sketches, creating various designs, and picking the best one; one can start by feeding some constraints into a computer. For instance, input the ballpark cost, the weight it needs to support, and what material it needs to be made out of. Then the computer can deliver thousands of design options. This is what generative design offers to the modern engineer.

True generative design is software that uses the power of cloud computing and true machine learning to provide sets of solutions to the engineer. This is in stark contrast to previous tools, such as topology optimization, latticing, or other similar CAD tools. All of these previous tools improved existing designs, whereas generative design creates a new design.

Generative design is also different from other existing CAD tools in that it can consider manufacturability. The generative design takes into account simulation throughout the entire design process. On the front end, the manufacturing method is considered, and the software will take care of simulating a given design’s feasibility. Thus, only designs that meet the necessary simulated criteria and are manufacturable are generated.

Generative design works best in conjunction with other technologies – 3D printing for instance. 3D printing makes it possible to quickly prototype and test new designs without committing to a costly and time-consuming custom manufacturing run. Also, there are no geometrical boundaries for a 3D printer. This means it can produce extremely complex structures that traditional methods, such as milling, are unable to manufacture.3D printing also facilitates mass-customization, i.e., it can print products tailored to a single specific need.

In addition to saving time, generative design algorithms can also create new products that were not possible before. For example, researchers are using generative design algorithms to analyse a patient’s bone structure and create customized orthopedic hardware on-the-spot using additive manufacturing processes.

Generative design is a fast-evolving field and new stunning applications are created literally every day. However, implementing generative design is not simple. Introducing generative design to a company or engineering department requires readiness and change among multiple stakeholders. It not only creates new products but completely disrupts traditional structures. It is difficult to master the software and a larger learning curve must be considered – it is definitely not a plug-and-play application.

However generative design is a powerful new way to approach engineering design problems. While AI and ML can’t replace humans, they can automate many of the tedious processes that create bottlenecks, ranging from optimization to aesthetics. Many of these capabilities are already present in modern tooling.

In the near future, items that we use every day, the vehicles we travel in, the layout of our daily work environment and more will be created using generative design. Products may take on novel shapes or be made with unique materials as computers aid engineers in creating previously impossible to conceive solutions.

The generative design takes an approach towards engineering that has never been seen before in the digital realm. It replicates an evolutionary approach to design, considering all of the necessary characteristics. Couple this with high-performance computing and the capabilities of the cloud; and the possibilities are limitless.

Keeping Cold Chains on Alert with IoT Analytics

Keeping Cold Chains on Alert with IoT Analytics

COVID-19 vaccine shipments have just begun. Is the healthcare industry ready with an infallible cold chain?

With multiple COVID-19 vaccines on the horizon, the next challenge is a cold (supply) chain ready for the onslaught of shipping vaccines across the world. It will not be simple to implement, considering the number of transfer points from manufacturing to administration sites. Vaccines are typically transported by air and then by road (usually trucks), with multiple stops and storage at the distributor before reaching the endpoint, where they once again go into cold storage. The last mile to the healthcare provider could be delivered to a nearby town by any means available.

Almost a fourth of vaccines are degraded by the time they arrive at their destination, due to incorrect shipping procedures. Losses from vaccines exposed to temperatures outside the recommended range are estimated at USD 34.1 billion annually, including product cost and wasted logistics expenses; a number that is likely to increase exponentially if you consider the cost of wasted COVID-19 vaccines.

Cloud platforms, blockchain, and IoT-enabled monitoring sensors provide real-time visibility into temperature changes once the vaccines leave the labs or manufacturing facilities … and the opportunity to check before there is damage.

IoT can transform cold chain management
Cold chains can collapse due to mechanical failures, heavy traffic, human error and even theft. IoT also helps the entire cold chain infrastructure function as a single unit, bringing in new levels of safety and cost-efficiency.

Using GPS-enabled packaging material, or by attaching RFID sensors to the actual shipment, loading device or crates makes it possible to measure the temperature, humidity and other variables affecting the shipment.

IoT devices transmit this data in real-time, sending alerts or notifications to various stakeholders when conditions are compromised. Supported by predictive and descriptive analytics, logistics firms can make any necessary adjustments to maintain the integrity of the vaccines, and even initiate a lockdown in case of theft.

How IoT devices work in the cold chain
IoT sensors can read light, temperature and humidity, in addition to manufacturing details like serial numbers. Armed with this data, vaccine manufacturers can specify rules such as temperature bands and program them into the sensors. The system can send alerts for deviations from the specified temperature range as well as the location of the shipment. Similarly, when a hospital receives the vaccine shipment, it can check the dashboard to view the chain of custody and other details thanks to the data stored in the sensors and accessible via the cloud. So even if the manufacturer missed/ ignored an alert about the vaccine being unusable, the hospital can reject the shipment because the logistics firm messed up.

Another method is to use scannable barcodes. IoT-powered barcodes can upload information to a cloud-based blockchain system. Barcode tags can monitor details like aggregate time outside the specified temperature band, and return a message to the scanner within seconds, confirming the safety of the vaccine or suggesting safety measures.

Unlocking cold chain insights
When equipped with timely and relevant data, pharmacos can optimize inventory and ensure safe vaccine distribution. Data gathered by IoT devices and sensors can be integrated with other external data for deeper insights to automatically trigger action without human intervention. The data gets processed at the edge of the network at the device level or local level. Cloud-based solutions can process tons of data generated by IoT, AI/ML and advanced analytics to deliver diagnostic and predictive insights in real-time for location, condition, consumption and anomaly detection.

Closing thoughts
Fail-safe cold chains are essential to distributing vaccines because any inefficiency could mean losses worth billions of dollars, or losing ground in the fight against a pandemic. In response, companies are now adopting technologies to better monitor cold chain shipments, improve logistics management, and achieve near-perfect visibility into the transportation process. They are now able to introduce the necessary checks and measures early on. Such data insights can also help build stronger networks and more resilient processes. For example, data can reveal underperforming or problematic partners, allowing companies to find better suppliers and service providers to be part of their cold chain.

If you would like to learn more about how analytics can help transform your cold chain, get in touch for an open discussion with our experts. Speak to us today.

Industrial IoT Analytics– The Shift to Smart Manufacturing

Industrial IoT Analytics– The Shift to Smart Manufacturing

For Industry 4.0, data analytics is an integral part of its operational strategy; enriching everything from vehicles and manufacturing to warehouses and marketing. There is a definite ROI in aggregating previously inaccessible data from the network and the edge, and analyzing it to increase efficiency, monitor performance, save costs and stay competitive.

The power of Industrial IoT data

The Industrial IoT is driven by a universe of sensors that enables accelerated deep learning of existing operations. These data tools allow for rapid contextualization, automatic pattern, and trend detection. Applying this to manufacturing operations allows for true quantitative capture of formerly “expert” qualitative operations. There are several powerful use cases in a digital factory that is enabled by analytics and Industrial IoT technologies, including the ability to:

  • Manage machines, processes, and people with speed and agility
  • Monitor factory assets in real-time by analyzing historical operational data to predict failure and fix it before it occurs
  • Apply Video Analytics to improve processes in real-time
  • Quickly simulate and compare the results of retooling an entire product line

Of course, success with these types of advanced analytics and Industrial IoT initiatives does not happen overnight. Employing analytics, sensors, and other related technologies can have a snowball effect on uncovering new efficiencies or business opportunities. For example, data analysis or analytics tools provide a way to more accurately identify potential issues in your processes that might be ideally suited for Industrial IoT initiatives.

Applying Industrial IoT data analytics

SMEs contribute to the health of economies and business productivity around the world. They are critical suppliers, partners and customers in nearly all industries –particularly in manufacturing where they become important intermediate suppliers, selling their goods into global value chains through larger local or multinational companies. That is why it is important that SMEs keep up with their larger business partners and customers in the Industry 4.0 revolution.

Improving productivity is the most obvious and tangible benefit of adopting Industrial IoT technology and the related data, but the benefits for SMEs go well beyond that. Industrial IoT can create value along multiple dimensions such as driving growth through improved products, improved customer service and engineering, better operations and planning, and more efficient factory management and enhanced support functions. A huge variety of devices connected to the Internet and share data through sensors every day, which when effectively collected, analyzed, and stored helps achieve a variety of benefits for SMEs.

Let us consider some of these benefits.

Improved equipment maintenance
Industrial IoT data analytics helps SMEs determine when factory equipment requires maintenance by measuring vibration, heat, and other important figures. Smart equipment can also send messages to operators about potential breakdowns, wear, and delivery schedules. Workers can see exactly how their machines are performing in real-time, and stay updated about potential issues. This not only facilitates regular equipment maintenance but also contributes to predictive maintenance. Sensor data allows maintenance to be scheduled at the optimal time, thus reducing breakdowns and maintenance costs.

Operations optimization and automation
With Industrial IoT sensors and analytics working in tandem, SMEs can automatically control processes that previously could only be tracked manually. For example, they get a comprehensive view of what is going on at every point in the production and maintain a continuous flow of final products, identify bottlenecks in real-time, and avoid defects. Humidity, for example, can have a negative impact on the quality of a paint shop and this can lead to rejects. Therefore, Harley-Davidson has implemented sensors in its paint shop to detect the humidity level. The ventilation fan speed can be automatically adjusted in order to assure a consistent coat.

Customer experience enhancement
IoT-enabled field service can dramatically improve customer experience. Giving technicians access to CRM data from their tablet shows them a detailed customer history. And they do not need to call the office to answer the customer’s questions. SMEs can also build upon predictive maintenance with business data like CRM and EAM. When the machine learning algorithm predicts an asset failure, they can connect to the EAM system and check the warranty. By automatically checking the warranty, they can prevent compromising warranties and reduce maintenance costs.

Data from wearables
We are not talking about data from smartwatches and fitness, but a new breed of industrial wearables. These new wearables promise to make difficult and often dangerous jobs safer and easier. For example, data from wearable gas detection sensors can track employee exposure levels and can then be displayed alongside their work schedule. This helps dispatchers adjust the schedule based on the worker’s exposure. Another use case is for logistics service providers. Sensors can detect driver fatigue and trigger an alarm to stop the driver. This helps improve schedules, routes and safety practices.

Location data
Location data could come from mobile devices, location beacons, GIS systems or even drones. GPS data from a vehicle can be combined with traffic reports to optimize delivery routes in real-time. SMEs could also place track-and-trace sensors on expensive mobile assets that often get stolen or misplaced. A vital IoT data application is using real-time location data to avoid vehicular accidents. Streaming real-time data from location beacons can help prevent fatal accidents. When a vehicle passes a beacon, the IoT application can automatically check whether the vehicle has the correct clearance certificate.

Inventory Management
IoT technology can eliminate the need to physically scan individual parts to get an accurate count or location. RFID chips – easily affordable – can be placed on products and remotely connected for real-time visibility into product locations and quantities. For SMEs manufacturing perishables like food, RFID tech can raise an alert when a product is approaching its expiration date. Optimizing the supply chain is a huge benefit of Industrial IoT data.

The technology of the future

Industry 4.0 is no longer a vision. Best-in-class firms are using analytics and Industrial IoT (IIoT) to make better decisions regarding assets, products, processes, and operations, and it is driving significant returns. IIoT analytics help SMEs get a better understanding of the manufacturing and supply chain processes, improve demand forecasting, achieve faster time to market, and enhance the customer experience. However, considering the scale and the complexity of the IIoT initiatives, successful adoption requires thoughtful orchestration, analytics and management of the tons of data the IoT generates.

Seeking assistance with data management is imperative

Managing data from IoT devices is an important aspect of a real-time analytics journey. This large chunk of data needs to be managed appropriately to reduce complex challenges. To be sure that an SME can handle IoT data demands, they need to build several capabilities such as versatile connectivity and ability to handle data variety, edge processing and enrichments, big data processing and machine learning, real-time monitoring and alerting, etc.

This can be overwhelming for an SME, especially when it is not even in the business of handling data.

Building IIoT data analytics expertise in-house can be challenging; especially for SMEs without the same financial, human resource or technology ecosystem options as larger firms. An Inmarsat study revealed that 72% of businesses have a shortage of people at the management level with experience in IoT, while another 80% reported a lack of skills among employees in IoT deployment. SMEs are wary of unknowingly tying themselves into a platform that may not last the course. That is why engaging an experienced data analytics player becomes critical.

How BFSI Firms can Leverage Data to Navigate through the Pandemic

How BFSI Firms can Leverage Data to Navigate through the Pandemic

Soon after the WHO declared a COVID-19 pandemic, there was utter chaos all across the financial world. Banks, NBFCs, fintech firms…all were hit hard by drastically pivoting market conditions and deteriorating credit quality among others. Lockdown situations in most countries and industries resulted in severe dips in cash flow with deteriorating corporate revenues and depletion of credit facilities. Governments across the world announced financial measures to ease payment pressures on individuals and businesses, such as extended moratoriums on loan payments, adding to liquidity woes.

Investors began pulling out their money, stock markets crashed along with oil prices, and central banks had to inject liquidity to keep the economy moving. Both the supply and demand sides dulled, thus impacting the economy. Uncertainty clouded investment decisions taken by investors and shareholders operating on financial markets, including securities markets.

And the problem will not go away soon. Moreover, the current downward trend could worsen which could impact the industry for years. The question everyone is asking is – what can help the BFSI industry sustain through to the other side of the pandemic?

Among other strategies, one instrument that BFSI firms can use to stir through this crisis and build further resilience is data engineering and advanced data analytics. BFSI analytics can help focus on spending patterns and customer behavior, primary transaction channels, fraud management, risk assessment, amongst others and help banks take steps from the what, to the why and finally, the how.

Risk Modeling
Poor credit quality will result in an increased number of default cases, more requests for forbearance and rising credit risk provisions. Banks need to cope with recalibrations of rating models and an analysis of credit portfolios in light of the pandemic. This will require collecting and sifting through massive amounts of customer and credit data. Analytics will process all that data at scale and perform quantitative risk analysis for better risk modeling, evaluating market risk, value at risk, accelerated credit review, and so on.

Liability Analysis and Delinquency Detection
Loan delinquency has become a bigger problem for banks during the COVID risk and will be devastating if it goes unchecked. Data analytics in BFSI plays a vital role in giving financial firms early warning predictions using liability analysis to identify potential exposures prior to a default. AI-based analytics uses drill-down reporting making it easier to detect criminal activities like fraud and money laundering by identifying transaction anomalies. Analytics helps issuers proactively use account pattern-recognition technologies and take proactive maintenance strategies by segmenting delinquent borrowers and identifying self-cure customers.

Growing Fraud in a Pandemic
The pandemic has provided the perfect storm for fraudsters to flourish, thanks to a more digital environment. Analytics sift through structured data (transactions) and unstructured data (emails, reviews, forum entries) and help BFSI companies identify potential fraud by analyzing the most recurring operational patterns regarding trades, purchases, and payments. Financial firms can use prescriptive analytics to evaluate their internal fraud control measures by looking at statistical parameters, data anomalies. AI’s high computation power will alert banks to potential fraud in payment, customer identification and so on, while ML algorithms will reduce false positives.

Credit Scoring
Various companies, especially MSMEs, are strapped for funds. They were just about making a comeback from the 2008 financial crisis when the COVID pandemic pushed them off-track once more. When evaluating them for financial support, banks typically use only credit scoring, which is not holistic and looks only at credit and financial details. This is not enough protection against loan defaulters. To determine a more valid credit score, BFSI analytics examines all available information –both structured and unstructured – using an algorithm to calculate the size of the risk the bank would take if they chose to underwrite that customer. AI-powered credit scoring models will reduce credit risk and enable decision-making and actions that are transparent and based on data.

Risk Hedging
Being able to sort out customers before they default on their installments helps banks avert disaster when the debt becomes overwhelming. Data analytics in BFSI allows banks to quickly adjust their hedging strategies across forex, commodities, equities, or fixed income as the pandemic situation evolves. They can use analytics to build portfolios and hedge risks by either setting a higher interest rate or offering a new payment schedule.

Liquidity and Treasury Risk
Liquidity stress models that were revised after the 2008 crisis are not fine-tuned to manage the liquidity crisis today, so BFSI companies need to pressure test and revise certain models. BFSI Analytics helps banks build credit line models with an additional layer of judiciousness and loan models with more flexibility to meet requirements during a pandemic. Financial firms can also use analytics to increase the flexibility of liquidity models for ad-hoc recalibration.

In Summary
To navigate the crisis brought on by the pandemic, Banking, Financial Services and Insurance sector companies worldwide must ensure that their business models, strategies and methodologies are fit for purpose and fortified with a solid recovery plan and governance models. They need to re-adjust their risk appetite statement and recovery thresholds by building a layer of BFSI analytics that can help them with:
• Managing liquidity, navigating new policies and preventing losses
• Model implementation and quick revision of risk models
• Flexible data visualization and risk analysis
• Monitoring trends and identifying emerging risks
• Insights into strategic actions
• Augmented underwriting powered by AI

In the midst of all this chaos, financial institutions have to be able to analyze new scenarios faster and learn from frequent updates to forecasts, business, funding, and capital plans. Data analytics will help companies in the BFSI sector to remain resilient and competitive in these challenging times.

Click the links to read more about Tibil’s Data Solutions and Industry Solutions.

The Analytics of Things – Why Data Analysis is Good for IoT

The Analytics of Things – Why Data Analysis is Good for IoT

Internet of Things (IoT) strategies are being adopted by industries world over to improve their processes and deliver a unique customer experience. From pens, lanyards, smart tags in stores and switches to air conditioners, refrigerators and traffic lights, IoT is being deployed almost everywhere.

But implementing IoT at scale requires managing millions of connected devices to gather and transmit data that is at once generated and essential for IoT devices. Naturally then powerful data analytics solutions become necessary to help businesses understand what this data is telling them. It can be a daunting task for any organization to build a functional, scalable and secure IoT network and datacenter without an experienced technology partner with the relevant expertise.

To understand the scale of data generated by these devices, we need to only count the number of IoT endpoints or a number of devices with each one capturing different parameters. This number runs into billions worldwide. Imagine the final volume of the data that is generated by these devices including sensors, actuators, tags etc. In fact, according to the Grand View Research, the global IoT analytics market size is expected to reach USD 57.3 billion by 2025 at a CAGR of 29.7%.
These staggering numbers could mean a data nightmare and raise several questions like:

  • Should all the generated data be stored and how?
  • How can we derive meaning from the data generated?
  • Is it possible to handle the high velocity of the data stream?
  • How do we secure IoT data and prevent misuse?
  • How do we enable reliable data communication between sensors, from sensors to applications, or even from sensors to a cloud?

IoT Analytics is an emerging area of data science and data analytics that can provide answers to these questions. The full capability of IoT for business use lies in connecting IoT to the data generated within the IoT ecosystem. There are various types of analytics that can be applied to IoT:

  • Streaming Analytics: Analyzes huge in-motion data sets, i.e. real-time data streams, to detect urgent situations and immediate actions and help areas like financial transactions, air fleet tracking, traffic analysis etc.
  • Spatial Analytics: Analyzes geographic patterns to determine the spatial relationship between the physical objects and helps in fields like smart parking.
  • Time Series Analytics: Basis analysis on time-based data which is analyzed to reveal trends and patterns and can be applied to weather forecasting applications and health monitoring systems.
  • Prescriptive Analytics: Combines descriptive and predictive analysis to help organizations evaluate the best action that can be taken to troubleshoot or improve a particular situation.

But do note that IoT Analytics is still an evolving field and is still dealing with complexities related to streaming and real-time data. IoT data comes in huge volumes, is highly unstructured, and differs in terms of format (text, image or videos). Moreover, while the operational technology relates to the data collected from temperature sensors, pressure sensors, tablets, smart manufacturing devices/tools, etc., the information technology it needs to be set in context to the data collected from enterprise systems, legacy systems, ERP, CRM, and finance systems.

Moreover, the total amount of data being collected may be so large that it may not be possible to move it to a central location. All IoT devices need to be connected and inter-operate. However, this requirement also raises data security issues, based on the age-old principle that a chain is only as strong as its weakest link. In IoT’s context, if the security on a specific vendor’s outdoor sensor is weak, and the sensor is connected to other devices, the likelihood of ‘indirect’ critical impact is high. Attackers can compromise the sensor and modify its data or exploit the connection to other devices to cause damage. Similarly, devices can also malfunction and transmit false readings to the system and throw IoT analytics off course.

Despite these challenges, IoT-generated data must be able to give organizations a clear picture of their desired outcomes in order to ensure that the analytics technology used to gain insight from that data is aligned with business needs. The good news is that analytics technologies have rapidly become advanced, and it is now easier for even newly generated data to be available for analysis almost instantly. IoT can be processed within a matter of seconds and can be retained efficiently and cost-effectively for a longer duration.