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Discover how SaaS-based Analytics has become a trending choice for better lending in 2023 Banks and NBFCs have shifted towards the Software as a Service (SaaS) model, in which the software is licensed on a subscription basis. This service’s benefits include reduced deployment costs for new software, reduced time to market for new products, and improved customer experiences. Banks and NBFCs need the service to improve business processes faster. Before the Covid19 breakout, SaaS was gaining popularity because of subscription-based trends. It provides greater flexibility and security. The Trend of Subscription-based Services in Finance Subscription-based software lets customers simplify procurement. Moreover, a lower cost of entry is highly attractive. NBFCs and Fintechs can now avail of advanced analytics systems at the lowest costs. SaaS-based Asset Classification and Predictive Analytics can be a boon for Fintech and NBFC lenders with lower lending capacities. Data security is the only factor that downplays SaaS analytics solutions. Easy-to-Implement Cloud-based Solutions From a high degree of on-premise and manual efforts to install and manage applications, the SaaS model allows for software applications to be hosted in the cloud, eliminating the need for local storage. The cost of acquisition and the need for software management is also minimized. At one time multiple users can use the model remotely and also offer more robust and secure data backups. Pay Only for Data Analytics Services You Need Most The lower cost of entry means that customers can categorize software investments as operating expenses rather than capital expenditures. Scalability benefits offered by SaaS mean that monthly or annual licenses can be easily customized based on changing business requirements. Since most activity is done from the data center, a lender's ability to launch and scale digital applications as part of their digital transformation strategy, is gradually enhanced. Yes, or No to SaaS? There is a lot of activity and some hype surrounding SaaS, but it is not the only option in the market and is not an entirely straightforward service. There are many factors to consider. On the positive side, SaaS can help a large enterprise achieve greater goals like the speed and agility of a much smaller company; thanks to low upfront investments, rapid implementation, and easy scalability. Also, it is a way to make a cost advantage in the short run, although the long-term cost savings are less clear. But, the same doesn’t stand for smaller lenders, NBFCs, PPIs, and Neobanks. Yet perhaps the biggest obstacle in the journey to SaaS adoption in the large enterprise space is the capability gap between established enterprise applications and still-maturing SaaS applications. Explore our cutting-edge SaaS-based advanced analytics products and connect with D2K fintech experts to discover the solution that best meets your banking needs.

Unlock cost-efficient data management, accelerate processing speeds, and automate big data analytics with cloud-based solutions Since the onset of the COVID-19 pandemic, the cloud is no longer a new technology. It has become a necessity to survive in the digital-first world. In Banking and Finance, Cloud Technology helps build cost-efficient data warehouses. These manage the huge amounts of big data generated daily. Mainly, the speed of digital user interfaces like Banking applications and web portals depends on processing speeds allowed by cloud applications. Secondly, by using a cloud-based storage huge amounts of data processes can be easily automated. Giving rise to quicker big data analytics. Accordingly, the role of cloud usage for big data is growing. Some challenges, considerations, and strategies are relevant to organizations across various industries that have embarked on their cloud journey. Planning the roles for including cloud-based storage and cloud-based digital applications. Does Big Data Equal to Big Problems? Yes, Big data can pose a big problem in cloud migration. Let’s see why. To understand why, let’s first see what cloud migration means. Cloud migration refers to the transfer of data, applications, and other business elements into a cloud computing environment. One of the first problems is on-premise infrastructure. For a perfect migration, connectivity between different data sources is essential. Data from on-premise systems can be streamlined for the correct motion of data – otherwise, it could affect business productivity and even cause downtime. Another problem is the loss of control over data. Especially, when we compare it to an on-premise structure. Big Data of FIs carries extremely sensitive information. The possibility of losing it is high when cloud migrations are underway. But one always has an ace up the sleeve, right? Whenever it ends up to cloud migration, Data Virtualisation is the ace. It has the power to convert the big data problem into a big data opportunity. Does Big Data Equal to Big Opportunity? Moving big data to the cloud unleashes tremendous benefits. The cloud’s scalable environment is far more cost-effective, and secure, and can be used to improve the speed, performance, and productivity of business operations. Ultimately, its value is acknowledged as a storage solution for higher Return on Investment. Data virtualization is explored as the key to unleashing these benefits and helping organizations to perform better. Providing one single, logical view like the bird's eye view of all data no matter where it resides, can enable businesses to simplify innovation. It helps manage big data in a cloud environment by creating virtual structures of big data systems. It ensures that teams can manage connectivity, respond to security fears, and fix all compliance requirements, as well as find whatever information they require. Here’s how – Create intuitive tools for sharing data while identifying correlations and patterns Build predictive analytics models to securely deploy them in the cloud Begin and analyze a data query within a minute to create a secure data warehouse Store and collect big data in the cloud at the lowest cost possible The different types of cloud migration strategies Cloud migration is the process of transferring an organization's data, applications, and IT infrastructure from on-premises servers to cloud-based environments. This strategic move allows businesses to leverage the scalability, flexibility, and cost-efficiency offered by cloud computing. However, cloud migration is not a one-size-fits-all approach, as organizations have unique requirements and considerations. To ensure a successful transition, different types of cloud migration strategies are available, each offering distinct benefits and challenges. These strategies range from a complete migration to a single cloud provider to a hybrid approach that combines on-premises infrastructure with multiple cloud services. Understanding these strategies is crucial for organizations to make informed decisions and effectively harness the power of cloud computing. Rehosting – Lift and Shift – Companies that support conservative culture or no long-term strategy to reap advanced cloud capabilities are well suited for rehosting. It involves lifting the stack and shifting it from on-premises hosting to the cloud. It transports an exact copy of the current environment without making extensive changes for the quickest ROI. Replatforming – It’s a variation of rehosting as it involves making a few further adjustments to optimize the landscape for the cloud. But the core remains the same again. This one is a great strategy for conservative organizations that want to build trust and engagement in the cloud while achieving benefits like increased system performance and cost optimization. Rearchitecting – also known as refactoring – means rebuilding applications from scratch. This is usually driven by a business's need to leverage cloud capabilities that are not available in existing environments such as cloud auto-scaling or server-less computing. It is the most expensive among all but also the most compatible with future versions. D2K Solution Architects specialize in assisting organizations in the banking and financial sectors with their cloud migration, data management, and digital transformation needs. By partnering with D2K, you can leverage their expertise to enhance operational efficiency, mitigate risks, and stay ahead in the rapidly evolving financial landscape.

Explore How the Cloud Drives Innovation in Finance and discover its Benefits in BFSI With new technologies and innovations becoming an integral part of Banking, some main implications are to explore and adopt new software architecture. Banks have to watch out for costs, mitigate risks, improve data processing speed, faster interoperability, and expand imprints in emerging markets. This is where the Cloud Hybrid models come to the forefront. A Hybrid Cloud model makes Financial Institutions competitive, as it streamlines business processes and secures data. India’s banking sector was in a nascent stage for cloud adoption before the outbreak Covid19. But now, with 30-40% enterprise cloud adoption, India has gradually grown into a top hybrid cloud market. The main goal and advantage of cloud migration is to host applications and their data in the most efficient way possible. Nowadays, companies migrate on-premises software and data to cloud infrastructure to enjoy new-age benefits. Cloud Migration for Applications and Data in BFSI Moving core systems and functions to the cloud can be a dream come true for performance growth teams. Two factors drive this trend – 1) Ever-changing user experience and 2) The drive to improve performance. Customers require improvised user interfaces. Here, Financial Services Companies keep up with changing consumer demands. As a highly adaptable platform, the cloud provides quicker time to market and significant opportunities for growth. Also, most importantly it lowers costs. Below are the ways, Cloud migration increases the performance of multiple lending and financial processes. Faster Scalability Clouds allow plug-and-play integrations to add new features to existing applications. It not only enables faster deployment of services through PaaS, SaaS, and Cloud Services but also improves systems agility to scale up effortlessly. It offers on-demand scalability of processes and capabilities to manage costs in line with user and business demands. Cost Reduction Clouds help evade costs associated with upgrading on-premises infrastructure, which is expensive and, in some cases, unreasonable. It also makes on-premises infrastructure management redundant, which helps banks to focus on value-added functions more closely. Business Diversification Cloud migration fosters increased operational efficiency and a better infrastructure for innovation, allowing Traditional Banking to keep up with the changing pace of the market. Implementing a multi-tenant PaaS on the cloud, banks will be able to offer state-of-the-art financial solutions to their customers including internet banking, online money transfer, ATM, and mobile banking at a much lower cost. Connect with D2K Banking Fintech Experts, the leading fintech solution architects, to explore innovative Cloud Hybrid models that streamline banking processes, secure data, and enhance performance.

With the right expertise and tools, financial entities are unlocking the potential of data to drive innovation and growth Exploring data and analytics (D&A) trends demand large-scale culture shifts. With technological rollouts taking major industries by storm, the urgency that comes with real-time data is intensifying. Here, Banks are expecting more from data, leaving just one requirement open-ended – innovation. New age data collection and delivery uses data lakes and accelerates analytics processes with AI/ML-powered solutions. Because aggressive scaling up of architecture is the only way forward, a few trends that deal innovatively with demand, have emerged. Where data is second to none, staying up-to-date with trends helps envision system transformation. Since data teams play a major role in driving this, we have a list of trends most Banks and Financial Entities should keep a track of today. Environmental, Social, and Governance Data The increasing availability of Environmental, Social, and Governance (ESG) data is enabling financial entities to develop comprehensive strategies for lending, credit monitoring, and collections. On a larger scale, the ability to build thematic strategies can be enhanced by the addition of Artificial Intelligence (AI) modules, which help investors analyze data in real time. Cloud Computing Cloud access lets financial entities proactively store, access, and innovate with data, enabling easy scalability for future operations. Internal and third-party applications via the cloud provide them with potential cost savings and performance enhancements while enabling teams to generate insights and analytics in a more cost-effective manner. Automation for Access to Real-time Data Many key insights and correlations across finance can now be automated using machine learning (ML), a subset of artificial intelligence (AI). This enables credit monitoring teams to analyze much larger data sets (from hundreds of columns to millions of columns) and new sources of alternative data (e.g. social media, credit card spending data, and financials of top executives). Machine Learning ROI Artificial intelligence (AI) and machine learning (ML) is showing no signs of slowing down. Frequently bringing new business use cases, the planned return on investment (ROI) still needs to be clearly explained to the board. Research predicts that by 2022, 90% of banks will explicitly mention AI as a core analytical competency, and investments will grow. Soon, boards will look for a return on this investment. Finally, trends need to allow you to anticipate change and manage uncertainty. Proactively monitor, experiment with, or then decide to aggressively invest in key trends based on their urgency and alignment with your strategic business priorities. To stay ahead of the curve it is crucial to work with experts in the field of fintech and data analytics. Book a consultation session with D2K's Banking Fintech Experts to learn more about how to leverage these trends.

Evolve business strategies with future-ready data ecosystems and prepare teams for advanced analytics Artificial Intelligence has undertaken mature roles in Finance. Creating reliable data banks is one of its main functions today. With this, we see an increase in data quantity and quality, leaving businesses with a simple-sounding, yet critical task to — 'make data work better'. A well-rounded, data-led strategy requires software and hardware overhauls. Because of the high costs for this, solution providers need a proactive approach toward helping lenders maximize benefits from costly systems. Here, quite a few activities help Banks and FIs turn data analytics into a core function. To begin, let's look at a challenge that data solution providers have solved with an innovative approach. 'Data Analytics as a Core Function' - Where Do the Challenges Lie for Banking? Banks and Financial Institutions (FIs) have lesser freedom in choosing data solution providers. Some factors like stringent regulatory compliance and security for sensitive details are roadblocks to outsourcing data-related tasks. Though, with web-based data management and computing, a few reputed companies have built innovative solutions to overcome these challenges. Customizable off-the-shelf products are being offered based on SaaS (Software as a Service) and Cloud modules for dependable upgrades to legacy systems. These web-based systems put complex data ecosystems in place. But, that’s not the end of it. After solution deployment, a question remains – Can data solution partners offer more to build data analytics into a core function? To build on that, here's what your data solution partner should offer. Training and Education Programs for Data-readiness Banks and Financial Institutions (FIs) need to invest in analytics education programs. After recognizing data analytics as mission-critical, stakeholders need to fund efforts to build a community that cultivates data-centered culture — not just internally, but with others in the industry as well. To improve the quality of data processes, a few members of Banks need to be proficient in understanding data dependency, functions of analytics dashboards, and report generation for a number of use cases. Here, for successful data usage, solution partners should simplify enterprise-wide data delivery to play their part in time-and-function critical decision making. Exploring new markets swiftly is possible where simplified data is easily accessible to multiple teams. Sometimes, data is required in real-time and should be easily shareable too. Data solution partners solve a large chunk of the puzzle, but data literacy is pivotal for further improving these processes. Moving further, Banks will need trained teams to explore and evolve data utilities that can be shared with other Banks and FIs. Majorly, data utilities will soon be blockchain-based. These boost cost-efficiency and quick development of data-centered systems. Many trends today show that using data utilities is the safest way to deal with data cleansing for higher-quality insights. Professing a Shift in Work Culture to Accommodate Big Data As big data sources evolve, quicker strategizing is inevitable. Analytics helps in adaptation to unseen scenarios, where real-time data saves the day. To tackle an increasing reliance on ever-evolving big data, a work culture centered around big data analytics readies teams to embrace proactiveness. As of today, brick-and-mortar banks use big data to remodel customer segmentation and build solutions tailored to changing industries and their customers. Where basic customer segmentation doesn’t address pain points in real-time, big data helps to formulate individualized profiles and monitor them closely, mostly 24-7-365. Moreover, credit card companies, retail banks, private wealth management advisories, venture funds, and institutional investment banks all need big data for new-age financial analytics. It helps in quick-ideating to build market-acquisition-and-retention services. Build Solution Architecture that Helps Maximize Data Usability Massive amounts of multi-structured data live in multiple disparate systems. With modernized solution architecture, data usability can be increased to improve data-driven – Data Warehouses | Banks need teams that understand and make use of the democratization of analytics. Well-planned solution architecture helps with democratization – simplifying data to increase self-service – for teams to derive comprehensive insights. Data Flow Automation | Insight generation for regulatory reports like IRAC, Ind AS, and IGAAP require data flow from systems for loan origination, credit monitoring, and risk-based supervision. Data flow automated using AI/ML-powered systems helps increase the pace for the generation of these reports. Cloud Implementation | Real-time insights around borrower portfolios need interconnectivity between external and internal data sources. Cloud-based solutions help build highly flexible databases for the easy inclusion of new data sources. Cloud-based storage also makes data ecosystems highly scalable.

Take a peek at what's next for Banking as credit monitoring and borrower surveillance intensifies Indian public sector banks collectively owed approximately 6.17 trillion rupees in non-performing assets in FY 2021. This value was much higher, at around 7.5 trillion rupees in FY 2019, indicating a slow but slight relief for India's economy in terms of non-paying assets. Surprisingly, the pandemic is being recognized to have eased this situation with its demand for quick digitalization. An important outcome – Data-first Banking – is helping lenders overcome difficulties in borrower account monitoring and recovery strategies. Data-intensive systems help Financial Entities build end-to-end processes, improve borrower relationships, and above all, get accurate risk assessments using massive volumes of data. Here, Credit Monitoring Systems are driving this transformation. A Step Closer to More Accurate Loan Default Predictions Where Banks face increasing pressure from regulatory bodies for cleaner data, predictive analytics is unlocking its true potential. To enable predictions of loan defaults, Early Warning Systems (EWS) use financial and non-financial data from internal and external sources. Where Credit Monitoring Systems (CMS) mainly monitor transactional data, Early Warning Systems (EWS) is for advanced analytics, offering analysis for alternative and ESG data too. For comprehensive credit monitoring, new age systems without Early Warning capabilities upgrade Banks' infrastructure to accommodate advanced analytics in the future. Here, credit monitoring systems solve a common problem — unintegrated data. Where data is scattered because of unintegrated systems a Credit Monitoring System plays a key role. Aiding in the unification of data from disparate sources, the deployment of this system initiates transformations for data integration, real-time data updates, and simplified information sharing. Mainly, it helps Banks build Centralized Data Repositories (CDRs). Let’s look at how CDRs help and what else is important to create a data-intensive, comprehensive credit monitoring ecosystem. Solution Architecture for Seamless Credit Monitoring A Credit Monitoring System (CMS) streamlines data from underwriting processes, transaction systems, and third-party service providers like credit card companies and more. The system compiles huge amounts of data, checks, and creates reports for covenants, automates periodic reviews, and offers analytics for statements (Stock statements, Financials, etc.). To bring simplicity to the process, a question solution architects ask today is – can the complexity be managed better? Future-ready, Modernized Core Banking Systems Modernization of Core Banking Systems (CBS) begins with building Centralized Data Repositories (CDRs) which can be accessed by all teams. The next step is to integrate data from all sources and build data pipelines for simplified data flow to various teams like collection agencies, borrower account managers, and business analysts. Where NBFCs are increasingly competing in the lending space, modernized systems should help Banks innovate faster. For example, cloud-enabled, web-based Credit Monitoring Systems, today help Bank managers reallocate funds through mobile apps too. Streamlined Reporting and Insight Generation Apart from driving the development of Centralized Data Repositories (CDRs), Credit Monitoring Systems open the gates for better regulatory compliance. Borrower tracking and reporting for a variety of financial metrics, including revenues, cash flows, and leverage levels can be easily automated to meet regulatory mandates. With the same data, bankers can analyze financial statements and shorten timelines for credit write-ups. Using Credit Monitoring Systems, calculations and statements are accurate with limited manual intervention. RPA and AI/ML for Intelligent Automation AI/ML-powered automation boosts the efficiency of the credit monitoring processes. Apart from making processes cost-effective, it helps Banks and Financial Entities reduce manual intervention and repetitive data checks. This simplifies data processes at very large scales. CMS equips Banking teams with AI/ML-powered automation, also known as intelligent automation (IA), and integrates it with application program interfaces (APIs). These systems pull information from borrowers’ accounting software, for any number of accounts. Also, optical character recognition (OCR), an AI-based function, helps convert financial statements and accompanying notes from scanned documents or non-readable PDFs. This saves time for these previously manually-operated tasks. What’s Better than a Credit Monitoring Solution? Early Warning System (EWS) is an effective monitoring solution for loan portfolios, prescribed by RBI back in 2015 to lower loan-loss contingency. The system puts in place a proactive credit monitoring practice using predictive analytics. Research shows the system helps FIs maintain a strong risk appetite, a higher return on equity, and a better capital yield. The warning signs of default-prone borrowers, monitored by EWS can be grouped into five areas: transactional, financial, non-financial, external, and statistical. The system sources both structured and unstructured data from multiple sources for accurate predictions.

Real-time data solutions enhance risk-based analytics to improve digital lending Big and small Banks have digitalized extensively to deal with the pandemic. Especially, for customer experience and engagement. AI-powered solutions like chatbots, cognitive routing, and smart search are significant digital improvements. Here, data systems are at the core of it all. One big question is – which systems should Banks improve first? Tech. stacks for both big and small Banks have different benefits for their customers. To enhance strategy for customer retention and acquisition, tech. stacks should enable Banks to sustain in the evolving digital sphere. Some technologies are helping smaller banks thrive. Digitalization has helped fortify market share where top Banks in India pose a threat. As analytics evolves, upgraded systems should help smaller players build on their ace – consumer trust. Small Banks' Long-Standing Relations v/s Big Banks' Lucrative Market Offerings In the digital-first era, the tables have turned to prioritize sustainability. Predicting a customer’s growth is more important than just having good products for quick lending. Smaller banks may have aligned their offerings to suit their customer base, but they'll lose out on customer acquisition and retention if they miss the bigger picture. Top banks in India will undoubtedly offer monumentally better services to the same customer base. One significant disadvantage for them is that smaller banks are trusted more, and enjoy mindshare earned over the years of providing dedicated services. Here, newer players are yet to win customer trust in quite a few niches. Moreover, with the increasing number of digital services by Fintech Lending Companies, there are no more barriers to the entry of bigger banks. Digital literacy is catching on gradually and trusted smaller banks can leverage this window by using analytics decisively. For example, in rural farming, difficulties in borrowing are prevalent, but these will decrease as agri-tech takes the center stage. With India at forefront of development, competition for a number of segments is expected to intensify sooner than later. Smaller banks need to make informed decisions to extend a helping hand — digitally. Rural Segment: What Digital Maneuvers do Smaller Banks Need? Top Banks in India are improving the customer experience for urban customers through the personalization of services. For smaller players, this is easily achievable with their consumer base and can be introduced with cost-friendly, mobile-first API ecosystems. But for the fast-growing rural and other niche segments, personalization requires – Real-time Data Analysis: Continuous re-calibration of risk models based on new data to reflect current market scenarios Data System Modernization: AI/ML-powered systems for quickly-delivered, actionable insights from predictive analytics Hybrid Customer Service: A flexible workforce to help with virtual assistance that helps improve customer experience The wave of digitalization that has coerced smaller banks to innovate is the same wave forcing top Banks in India and fintech companies to collaborate with each other. In the case of rural segments, top Banks in India are not far behind. They are aggressively partnering with agri-tech companies dedicatedly working on empowering rural communities in India. How Real-time Data for Analytics is Helping Small Banks Lead Better? If smaller banks want to capitalize during this initial phase of digitalization, a few analytical systems that mirror bigger banks’ work ecosystems have to be put in place. Majorly, the required real-time Banking Data systems are easily adaptable through Software-as-a-service (SaaS Subscription) offerings – Supervision of a Banks Risk Portfolio – Assessment of a bank’s risk profiles for timely and accurate data reporting for RBI reports and business analytics. Fraud Detection and Prevention – Predictive analytics to pick up on minute differences in transactions and determine their legitimacy in a quick, unquestionable manner. Managing Credit Card and Loan Default Risk – Predictive analytics for possible defaults by credit card holders and loan debtors, and roadmaps for customer-friendly collection. Unsurprisingly, customer lifecycle analysis can be sourced from insights on customer behavior monitored in the lending process. The analytics involved require Banks to explore various angles to look at data coming in from both basic asset classification and client relationship management solutions — Modeling Customer Lifetime Value – Predictive Analytics for the net profit attributed to the entire future relationship of customers with a bank for offering value-adding services. Newest Intuitive Marketing Modules – Predictive analytics in journey mapping and hyper-personalization for customers in borrowing, debt collection, transacting, and marketing.

Indian banks are actively embracing blockchain technology for enhanced security, transparency, and efficiency in their operations India’s largest public sector bank, the State Bank of India (SBI) along with 14 other national banks has formed a new company called the Indian Banks’ Blockchain Infrastructure Company Private Limited (IBBIC) for designing, building, implementing, and commercializing blockchain. The system behind the idea is also known and more commonly recognized in India as distributed ledger technology (DLT). This is mainly to help deal with a persisting problem of processing Letters of Credit (LCs), GST invoices, and e-way bills. The industry expects the system to go live in the latter half of 2022. Moving forward, the modernization of domestic LCs will be followed by complex use cases such as collateralized loan disbursal, deeper credit rating, and transaction traceability. In another corner, open banking is set to come in all guns blazing with the introduction of Blockchain-based open banking in India, where a main player to watch out for is Banglore-based Polygon. How Else Will Blockchain Make Waves in Finance in India? Blockchain technology is set to revolutionize various sectors in India beyond banking, paving the way for transformative changes across industries. By enabling secure and efficient data sharing, reducing fraud, and enhancing trust, blockchain is poised to make waves in India, driving innovation and bringing about unprecedented levels of efficiency and accountability in diverse areas of finance. Money Transfer Heavily influenced by Bitcoin, transfer apps for a variety of cryptocurrencies are exploding in popularity right now. The system that backs this – Blockchain – is especially popular in finance for the money and time it can save financial companies of all sizes. By eliminating bureaucratic red tape, making ledger systems real-time, and reducing third-party fees, blockchain can save the largest banks $8 - $12 billion a year. Smart Contracts Smart contracts are contracts with the rules of the contract enforced in real-time on a Blockchain. It eliminates middlemen and adds levels of accountability for all parties involved. This saves businesses time and money, while also ensuring compliance from involved participants. Blockchain-based contracts are becoming popular as sectors like finance, government, healthcare, and the real estate industry explore their core use. Which is, decentralizing trust-based transactions with an assurance of zero-fraudulent activity. The DEA's report on Blockchain in India mentions that state governments are expected to examine the feasibility of DLT for land records and management. Government Finance One of the most unsurprising applications of blockchain is for the improvement of government processes. Apart from securing government documents, Blockchain improves bureaucratic efficiency, and accountability, and can reduce massive financial burdens. In India, the official report suggests that blockchain / DLT use cases will be explored by the Department of Economic Affairs, Reserve Bank of India (RBI), Securities and Exchange Board of India (SEBI), Insurance Regulatory and Development Authority (IRDA), Pension Fund Regulatory and Development Authority (PFRDA), and Insolvency and Bankruptcy Board of India (IBBI). Moreover, it also mentions that the state governments must examine the feasibility of using DLT for land-record management. Subscribe now to receive the latest updates, industry trends, and expert analysis from D2K Banking Fintech Experts. Don't miss out on valuable insights that can shape your organization's blockchain strategy!

The Asia-pacific region and Europe leads the way for Banks in Building APIs for Vast Ecosystems Digital mayhem has increased connectivity and collaboration, and there’s no going back. As a result, being present in niches is a newly acquired superpower for financial institutions. Banking as a Service (BaaS) enables Banking services through various unconventional digital channels with easy-to-integrate digital add-ons known as Application Processing Interfaces (APIs). APIs are a pivotal addition to Banking today as it helps explore new sources of data collection to evaluate customer behavior. This is important especially if brick-and-mortar Banks are looking to match the potential of their younger counterparts – neobanks. What is an API in Banking as a Service (BaaS)? Open Application Programming Interfaces (APIs) let companies create new business models by reaching customers through app integrations on another company's digital (web or app) platform. For a Bank, this comes into play when Fintech and NBFC companies are looking to collaborate for digital-first banking, debit and credit cards, loans, and investment services. A good example is an e-commerce portal like Amazon. With APIs, Banks can offer consumers the option to buy from Amazon using their digital credit card or debit card service, and even offer discounts, offers, benefits, as well as buy now pay later (BNPL). Here, like in all other BaaS collaborations, the BaaS model helps with the assimilation and redistribution of data, for both parties involved in offering the digital financial service. In the case of Fintechs and NBFCs with digital apps or websites, they can offer products and services to their customers/clients from Banks — by directly consuming the Bank’s APIs or by co-creating one. Why has BaaS Become an Unmissable Opportunity? Even though its importance is ever-growing, Financial Institutions are lagging behind other industries in API adoption because of stringent regulations. But as regulators are rapidly refining the rules, interconnected digital banking will soon take the center stage. Studies suggest the Asia Pacific region and Europe are leading the way for Banks in creating and integrating APIs in vast ecosystems.  Both regions were early adopters: Wherein Asia’s integrated services in e-marketplaces and mobile clients are prevalent, European countries implemented a well-thought API-based open banking scheme under a regulation imposed by the Payments Service Directive 2, started in September 2019. APIs for BaaS: Its Role in the Modern Business Like every other business, banking soon catches up to trends for consumer engagement. The concept of partnerships is not new, APIs are just a modern way to create well-connected marketing ecosystems. By using APIs, banks share the control of user experience with their partners, which changes the relationship of the third party from a vendor to a partner. For the once slow and closed banking industry, this is a huge change. Banking APIs include account authentication and information sharing, capabilities for analytics, loyalty programs, consulting, and payment processing. In major developed and developing economies, regulators are forcing banks to share customer data and create a shared space for data validation, which the usage of APIs thrives on. Since the trend is here to stay, here’s what banks should keep in mind while innovating with the connected ecosystem of APIs for a well-planned Banking as a Service (BaaS) offering. Understanding the Competition: API-based BaaS v/s Neobanks Neobanks is probably the best example of BaaS in action. In many cases, these Digi-first banks are not stand-alone organizations but simple add-ins on top of traditional banks. In that case, Neobanks provide an upgraded Digi-finance experience for its parent Bank. Superior service, lower fees, and personal touch are some offerings of neobanks. Mostly, big loans and large financial operations are ignored; short-term loans, quick deposits, and partnerships with well-known retailers are prioritized. For banks, the same could work well, as APIs can function as an interface to connect applications while sharing both data creation platforms as well as data. Being watched for its impact on 'Financial Inclusion' in India, ‘open banking’ or ‘banking through open APIs’ will soon see a surge because of new market segments and regulations. Some Areas Banks Can Focus on for a Sustainable Ecosystem? An article published on Open Future World explains the process from planning to execution in detail, and here are some excerpts that cover how to connect the dots for sustainable API integrations – Digital Product Definition – API Design, Data Scope, and Plans for Value to Be Added To begin, there are three questions that your team must answer: Why do we want to implement the APIs? What tangible results do we want to achieve with these APIs? How do we plan to run the API program to achieve these results? API Exposure – Innovation as well as Adherence to Market standards The simpler the integration is, the more partners will be connected to your product, and the greater your revenue. Creation of Indicators for Quality Management Using analytical tools is the only way to evaluate progress. Here are some important topics to configure in your analytics tools to measure the success of APIs – Business Value – revenue per API, cost of APIs, generated payments, successful API calls, number of customers using the API Customer Experience – Active users, response time and availability, and number of Third Party Provider (TPPs) offerings used by the core customer base Platform Usability – Number of TPPs within the ecosystem, number of transactions per TPP, number of TPPs per API D2K Technologies is a trusted banking data and analytics solutions provider, innovating for digital financial ecosystems since 2001. Get in touch with D2K Banking Fintech Consultancy Experts for more information on roadmaps for deploying technologies for comprehensive data analytics.

Finance leaders considering modernization can finally look beyond large-scale migrations Legacy systems are machines of the past, still capable of leading us into the future. This is mainly because it has worked as a base for data processing and data generation till now. These can easily be migrated to newer platforms but, let's see whether it is worth the cost. To answer this, Modernization is a buzzword used by big Banks looking to compete with innovative Fintech disruptors. In sync, instead of migration, a few additions to legacy systems can better transform the output for business intelligence and data inclusivity. Also being termed ‘Core Revival’ – the process explores the benefits of cloud implementation, Artificial Intelligence (AI) and Machine Learning (ML) system additions, automated Big Data sourcing tools, and Blockchain. These have immense potential in the long run, but we are just getting started and are yet to see the extent of their power. Though in the process of exploration, we are realizing that along with systems for newer types of data we need infrastructure that easily accommodates arriving technologies. Future-Readiness for New Infrastructure and New Data To avoid the mistake that made legacy systems a burden, a newer trend professes the adoption of flexible, web-based data management and analytics solutions. These solutions are deployed directly through the cloud or are offered with Software as a Service (SaaS) subscriptions. Core revival initiatives introduce leading-edge technological changes into existing IT ecosystems. Here, technology teams can account for risks when old and new technologies intersect. Here, close collaboration with data operations teams helps anticipate and manage risk in a manner that doesn’t hamper growth in current and future situations. With current-day deployment methodologies, finance leaders considering modernization can look beyond large-scale migrations. For example, when it comes to large cloud vendors, a common practice is to move one component at a time. Moving existing systems to low-code platforms promotes cost-neutral, easily-adaptable options for establishing a future-ready foundation within core systems. The Pandemic Push: Where is Modernization Headed? Even as we get back to the new normal, the accelerated rate of business transformation, read: digitalization, will stay the same. Along with promoting a collaborative ecosystem, businesses have realized the importance of preparing for disruption. Since replacing existing systems are the costliest modernization taking place, new-age alternatives of cloud-based systems have come to the forefront. Banks can now deploy systems quicker, increase scalability, and magnify analytical capabilities cost-efficiently. Here we see how real innovation isn’t about the next best thing; it’s about developing an ecosystem that supports continuous innovation. Let's take a look at a few functions of modernization that are preparing businesses for improved banking in the digital age — Cloud Systems for Simplified Collaborations Migrating major systems to a cloud helps Banks and FIs to share business functions easily. Cloud-based systems increase the capacity to collaborate with data and also allow partners to use their business models and offer their services using APIs — bringing forth the newest shift towards the prevalent use of Banking as a Service (BaaS). For a smooth transition, banks can decide whether to approach the migration through any one of the two possible migration methods – the lift and shift model and the transformation model. Either way, businesses have to be meticulous in the selection of the offerings from their cloud provider, which are – Software as a Service (SaaS) – Applications that end users can access through navigators, browsers, and web interfaces using Internet protocols. Platform as a Service (PaaS) – A container within a programming environment, libraries, and tools that support the application developments. Infrastructure as a Service (IaaS) – Virtual machines, storage memory, database service, and network components. Integrated Data Systems with Revamped Architecture Banks need teams that understand and make use of the democratization of analytics. Well-planned solution architecture helps with democratization – which means data is made available at multiple levels, for those who have the skill sets to derive meaningful insights. As all big data analytics is driven by metadata; all records are linked based on combinations of attributes within the data. Using statistical techniques for integrated systems, collective entities are identified and collapsed to produce single views of vast datasets through a single interface. Insight Generation from Reconstructed Data Flow Insight-generating systems using AI/ML provide actionable data for mandatory regulatory reports like IRAC Norms, Ind AS Reporting, and IGAAP Reporting. Automation of multiple data collection processes is teamed with automated data processing for these insights. Systems modernization for insight generation requires integrated solution architecture consisting of seamlessly interconnected internal and external data sources for Transactional, Financial, and other Big Data. In building a Data Lake to store and channel data flow to respective teams, Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Programming (NLP) are important additions. These manage data with automated sourcing, cleansing, insight generation, and submissions. Get in touch with D2K Banking Fintech Consultancy Experts for more information on roadmaps for deploying technologies for comprehensive data analytics.

A ‘Comprehensive Approach’ should focus on Data Integrity for multiple teams involved in legal case closure CRisMac Integrated Legal Management System (ILMS) is a Data Management Software for Legal Cases arising from loan defaults. As an information portal to simplify collaboration it increases the efficiency of the legal efforts of lenders. An increasing number of Banks and NBFCs can adopt the solutions to digitize the extensive process of recoveries and legal proceedings. An effective legal solution allows Lenders to open and track the progress of each case. The system facilitates information-sharing between a Lender’s internal and external litigation teams and enhances regulatory compliance by storing information for further use. During lengthy litigation processes, timely information sharing helps increase recoveries from deteriorated assets. The solution saves costs by enabling the timely generation of notices, tracking of dates and schedules, and decreasing timelines to complete settlements. A Modern Outlook Bringing Agility to Litigation CRisMac Integrated Legal Management System (ILMS) is an advanced platform built to carry out different operations with a single solution. Overall, it boosts agility during litigation. With its cloud-based storage, the system initiates digital transformation for information management, important legal measures, and process-oriented credit recovery. By centralizing information, ILMS enables better communication between multiple banking departments, external legal firms, and clients. Furthermore, it helps control delinquent loan accounts and offers detailed overviews of the legal information of borrowers. Features of CRisMac ILMS for Enhanced Litigation Processes As technology has improved, litigation is being managed by end-to-end systems that are effective right from the time of loan default to the completion of a settlement or write-off. Some areas for bad loans that CRisMac ILMS improves are – Repository for Virtual Files: A single repository stores pending issues, actions, and more, and enables intimations through timely alerts and reminders. A virtual file is maintained for each suit filed containing copies of important documents and relevant details. Web Application-based: Without installing any software on users’ PCs, Laptops or Mobiles, CRisMac ILMS helps you plan, initiate, assign and review all legal proceedings in courts in a few simple steps. It uses modern web software to provide hassle-free and easy-to-use dashboards that source data from highly-secure and scalable databases. Checklist and Scheduler: CRisMac ILMS offers complete visibility about the status of defaults and helps you create a checklist for required documents to prepare cases for multiple court filings. Multiple teams can access documents through highly-secure log-ins for a centralized database and schedule the next action and its performer. Empaneled Agencies Analytics: The solution offers easy accessibility to data for Advocates, Valuers, Detective Agencies, Recovery Agencies, Resolution Professionals, and investigation. It also tracks efforts by the members of these agencies and reports activities in real-time. CBS-compliant System: The solution is built to source data from multiple internal and external systems including Core Banking Systems (CBS). With the capability to keep track of recovery and litigation efforts for every type of Non-Performing Asset (NPA) it continuously populates NPA datasets using an auto-syncing feature. Simplified Fraud Reporting: Significantly reduce the time and costs associated with bank fraud by streamlining the opening, tracking and resolution of frauds. Moreover, CRisMac ILMS reduces the costs of regulatory compliance with an all-inclusive database of litigation details. Where regulatory compliance is dependent on the ability to track defaulters’ activities, a Legal Case Management Solution helps lenders seamlessly track the progress of each and every case. Get in touch with D2K Banking Fintech Consultancy Experts for more information on roadmaps for deploying technologies for comprehensive data analytics.

See what's needed for banks to improve big data analytics systems and its increased scope Banks that tap into big data trends and craft strategic advances are leaders in the truest 21st-century sense. But, identifying trends and aligning them to business goals is not the only task at hand. One pressing requirement is – a bank’s current infrastructure needs to keep up with the pace of advancement. For current-day systems, big data analytics is a business’ catalyst in better understanding consumers and the lengths of operational possibilities. Essentially with infrastructure upgrades like cloud computing becoming mainstream, and real-time access to big data becoming a reality, strategizing has evolved ten-fold. Multiple analytics providers have ready-made solutions to tackle the current requirement. But, for Banks looking to ace analytical functions in the now and the near future, business leaders need to ready system architecture for capabilities trending in other businesses too. Which Trends Should a Bank's Analytics Systems Ace? Add big data to the equation, and the need for faster data processing becomes a priority. Real-time data processing can be leveraged in multiple scenarios, and since everything is going digital henceforth, knowing the heights that transformation will reach help prioritize. The vast applications of data analytics and rise in DevOps teams are a result of improving data science approaches for big data generation. In maximizing results from data, here are a few trends your lenders' technology partners need to look out for – Increasing the Role of Alternative Data in Analytics Some major applications gaining ground are data enrichment to enhance underwriting, smart lending and credit scoring, and historic data for forecasting and predictive algorithms. Data as a Product (DaaP) with Data Subscriptions Data as a Product (DaaP), facilitated through Data Subscriptions, helps banks and lenders with pre-sourced good data without the hassle of revamping data collection infrastructure. Hybrid clouds for Improved Interconnectivity A hybrid cloud is an IT infrastructure that connects public clouds and/or private clouds to offer orchestration, management, and application portability while creating a single, optimal cloud environment. Banking Functions that Leading Analytics Systems Tackle Leading big data analytics systems offer actionable insights. The systems enable banks to draw conclusions about the segmentation of customers, better understand transaction channels, collect feedback based on reviews, and assess possible risks to prevent fraud. Currently, leading big data analytics systems for Banks and Lenders are – Early Warning Signal Solutions enable predictive analytics to help banks predict defaults long before they happen. These systems require data subscriptions for quality inputs on the borrower's financials, transactional behavior, market position, and other business factors. The 'advanced analytics' system uses artificial intelligence (AI), machine learning (ML), and neural linguistic programming (NLP) models to collect and process the information on potential defaulters, helping banks strategize for the best recovery practices. Risk-Based Supervision is a prescribed model for regulatory supervision of Financial Institutions around the world. Reported data is expected to be quantitative as well as qualitative, and is broadly expected to cover categories – capital, credit, market, earnings, liquidity, business strategy, operational risks, internal control, management, and compliance risks. An RBS System equips Banks with enhanced compliance and risk management practices, with the added advantage of a comprehensive MIS system for internal decision making. Management Information Systems with Automated Data Flow (ADF) for Regulatory Reporting offers the highest quality of data to business leaders as well as regulators, with a single system. New-age MIS systems for Banks and Financial Institutions are benefitting from the regulator-mandated automated data flow with the creation of ‘golden datasets’. Golden datasets are clean, validated, integrated datasets, which lets businesses identify function-specific use cases for data and put processes in place to join or reconcile, and ultimately use the exact data for multiple functions. Insight Generation and Research Tools for Regulatory Reporting offer actionable data for IRAC Norms, Ind AS Reporting, and IGAAP Reporting. Automation of multiple data collection processes uses data available systems for analytics. Systems for insight generation utilize integrated solution architecture, multiple data sources, and data lakes, and also deploy advanced Artificial Intelligence (AI) and Machine Learning (ML) for higher-quality data.