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With mobile banking in place, see how financial institutions can cut costs tremendously Post-Covid India looks a lot different from its predecessor. Once a cash-rich, thriving economy, India is now a technologically-enabled economy fueled by a cashless ecosystem. Thanks to government initiatives like UPI, an estimated 70% of Indian online adults with a bank account do their banking on a mobile application or website using their smartphone. Surely, it is a clear window for banks to convert into a digital outlet simply through a strategic mobile banking solution. Unfortunately, however, the timeframe for being a tech-first bank has trickled down exponentially over the last few years. Aggressive competition coupled with a lack of know-how disables the possibility of a clear foresight. But on the other hand, building the right technological stack for a centralized architecture that governs all the processes of a bank in record time makes it an impossible task in itself. This is where centralized loan origination and loan management solutions come into play. These systems streamline operations and enable a successful mobile banking strategy. Mobile Banking: The Next Frontier? Mobile banking is undoubtedly the next-generation banking methodology. Here is a list of benefits describing how mobile banking could help beat the competition in its space. Unparalleled Availability Unlike traditional banking infrastructure/branches, mobile banking allows banks to serve their customers all the time. One can access funds, take a loan or check one balance anytime, all through the comfort of the home. This unparalleled availability differentiates mobile banking from traditional models. Unlike traditional banking models that require customers to visit a physical branch during specific hours, mobile banking is available 24/7, allowing customers to manage their accounts from anywhere, at any time. The ability to perform transactions, check balances, and receive notifications on the go has made mobile banking a game-changer, setting it apart from traditional banking models. This unparalleled availability has enabled customers to take complete control of their finances, making mobile banking a vital component of the modern banking landscape. Help Banks Cut Costs Traditional banking strictly bound customers with paper-based transactions. These traditional solutions elongate different banking processes for the customers and increase the cost overheads of a bank. However, with mobile baking in place, banks and other similar financial institutions cut costs tremendously and narrow down the time of a particular task with a simple intuitive interface. Better ROI than Traditional Counterparts Studies on mobile banking users revealed that an average banking institution could generate incremental revenues and reduce attrition rate by an average of 15% by simply increasing mobile banking adoption rates. The study also found that an engaged mobile banking consumer can help in: Using a higher number of services Increased loyalty with a preferred banking partner An increased amount of transactions Concluding Remarks These factors mentioned above make mobile banking customers turn into better customers with more transactions. It also helps banks in all aspects and hence having the right mobile banking inclusion strategy in place is a win-win for banks. Ready to take banking experiences to the next level? Embrace the convenience and efficiency of mobile banking today! With more transactions and better customer engagement, mobile banking is a win-win for both customers and banks. Don't miss out on the benefits - talk to us about implementing the right mobile banking inclusion strategy for your institution.

See What Technology Uses Big Data to Warn Banks about Accounts for Potential Fraud ABG Shipyard Scam (Rs. 22,842 Crore) Nirav Modi PNB Scam (Rs. 14,000) Kingfisher Scam (Rs. 10,000 Crore) India’s biggest banking scams surfaced when the deed was already done, and there was no way to recover losses. Diving deep, we discover that in most scams, the banks’ employees facilitated the looting; raising questions about the ethics of the employees, the credibility of modern banking solutions, and the interplay of human intervention with machines. As a banking and analytics company, one pressing question that we repeatedly ask ourselves is, “Can our systems detect such frauds, especially when the internals of a bank were involved?” Unfortunately, the answer to this question is not a definite yes or no but is rather subjective, even despite being an advanced fraud detecting system. Understanding Banking Frauds Before we think of avoiding risks, it becomes essential to understand the nature of banking fraud. Preventing fraud ranks first in the priority of banks. As seen globally, a whopping US $ 3.5 trillion is lost to frauds, re-confirming the firm need for a fraud detection tool. On the other hand, as customers switch to online payments across devices and geographies, there is more ‘surface area’ for fraud to occur. To cope with technological transformation, fraudsters have even adopted modern-age tools. For example, a group of tech-savvy fraudsters could employ modern-day business solutions like machine learning to defraud banks and hence cannot be ignored at all costs. What has generally been observed in such cases is that conventional wisdom and practices fail to stop financial crimes. However, with big data analytics coming into the picture, there has been a rich combination of data and technical know-how to combat fraud. Further, in some cases, big data does not only stop exposing the fraudsters themselves but has proven useful in identifying their networks, people, places, and processes. Fraud Detection and Mitigation The process of fraud detection and mitigation starts at a very early stage, even before the loan is sanctioned. Capital once paid as a loan is difficult to recover, and hence banks need advanced decision-making capabilities fueled by data. This is where Artificial Intelligence (AI) and machine learning step in to identify such instances early while reducing false positives. Further, as more and more financial crimes were surfacing each day, the Reserve Bank of India (RBI) had prescribed strong measures to be adopted by the Banks to guard against such incidents. Key recommendations include setting up a transaction monitoring group within the fraud risk management group, alert generation and redressal mechanisms, and dedicated email IDs and phone numbers for reporting suspected frauds, which generally could not be addressed by traditional methods for identifying fraud. Concluding Remarks Using Early Warning Systems, a software solution, banks can analyze an incoming transaction in less than 300 milliseconds, ensuring that fraudsters (both individuals and companies) get detected as early as possible. With this advanced machinery in place, bankers can spot fraudulent behavior and withhold payments or loans that appear to be otherwise. But what could not be factored in is the intrinsic behavior of bank personnel, opening new frontiers to technological advancements in banking.

Intelligent Decisioning Engines Use Multiple Alternative Data Sources for Advanced Profiling Lending is witnessing a paradigm shift with digitalization. Across the globe, consumers’ demands are metamorphosing to embrace end-to-end digitalization. These systems help boost customer-centricity enabling quicker, automated, and streamlined journeys. According to a BCG report published in 2020, over $1 trillion in retail loans will be disbursed digitally in the next 5 years, and the digital footprint of consumers will increase to 75 percent from 50 percent by 2023 in India. According to TransUnion CIBIL Market insights, Indian fin-tech players loaned more than twice as much to millennials and Gen Z consumers under the age of 30 in 2020 as compared to traditional banks. But, progress doesn’t come without problems. Fintech has helped digitalize major processes, but it has struggled with underwriting. Traditionally, manual underwriting was the only way to disburse loans, but Fintech services help source borrowers’ information to simplify credit decisions. Today, digital lending solutions run advanced statistical analyses to derive the loanable amount. Borrower Data Simplifies Credit Underwriting Underwriting is a complicated process, but the inclusion of modern data analytics techniques has revamped the entire process. Reduction in turnaround time and accuracy in decision-making are two notable benefits. Credit scores were considered the basis to underwrite the creditworthiness of a user, here Fintech has changed the trend by including other sources such as FOIR, CIBIL score, loans taken, alternative consumer data, etc., which helps generate deeper insights and improves the accuracy in decision making. Why must System generated Underwriting be implemented? Quicker Lending Digital lending business models are increasingly proving to be more than just cost-effective, it saves time too. Lenders are able to execute real-time data assessments for application approval or rejection. It enables quicker loan decisions and better customer acquisition. Lending Experience Lending companies that use digital underwriting processes can improve customer engagement. As the entire process is run on apps and websites, lenders can shift their focus to offering value-adding services with a better understanding of pre-qualification journeys. AI/ML-driven Accuracy Data sourced by digital lending solutions help with a 360° view of a borrower’s financials, credit functioning current market standing, risk scores, outstanding loans, etc. This enables AI/ML-powered systems to run calculations, considering broader parameters. Get in touch with D2K Banking Fintech Consultancy Experts for more information on roadmaps for comprehensive data analytics in digital lending.

Systematic improvements in the present-day Banking Early Warning Systems use advanced AI/ML algorithms A decade and counting. It has been over a decade and a half since the Lehman Brothers collapsed. An event marked the onset of the global financial crisis of 2008. 2008 came with financial shockwaves, the worst in 70 years since the Great Depression. Hundreds of thousands of people lost their jobs overnight. Countries needed financial aid to bail out their chief financial institutions while countless US dollars were pumped into the system in a desperate attempt to revitalize the economy. It took years for countries to overcome the shock and several commissions were tasked to prevent such disasters in the future. One such EU-funded commission that came into the limelight gave birth to what we call today Early Warning Signals or EWS system. But the real question is, 'Can software prevent the next global financial crisis? And are we ready for it?' Here’s an insider’s outlook. The Report: Knowing When to Alarm The commission unit tasked with formulating the Early Warning System concluded, “Systemic events are intimately related to a banking crisis. Even a limited banking crisis may lead to the failure of the banks involved, causing a collapse of the entire Economic System.” This helped in the formulation of the earlier versions of EWS where banks closely monitored the credit structure, and the framework for Basel III was created. Most recently, India is on a mission to combat bad loans. The state’s central bank, Reserve Banks of India (RBI), made it mandatory for banks to implement the software to monitor the situation closely. This came as a boon for the economy as Early Warning Systems do work, even on a scale. Over the years, bad loans have decreased substantially, and a positive sentiment exists in the market for EWS and related systems. These systematic improvements in the present day EWS, that is now powered by advanced AI/ML algorithms, give machines power to suggest suitable measures in order to curb losses as fast as possible. EWS as a Building Block Early Warning Systems, today, act as a building block of most banking infrastructures. These systems generally capture raw data from various sources and segregate these data into three major forms. This enables banks to have a bird’s eye view of the entire credit structure. The system also gathers micro data trends, enabling it to produce signals at all levels. An imminent example of the same in recent times is the global crisis of 2020 triggered by the COVID-19 pandemic. Most nations had the exact blueprint for investments to avoid derailing the entire economy. The Final Call: Can a Piece of Software Prevent the Next Financial Crisis? The short answer to this question is yes. EWS along with advanced AI/ML algorithms make an undefeatable combination and can even disrupt some of the major banking frauds in milliseconds. The same is true for financial ecosystems and with the combination of other banking and analytics software, the next crisis could be prevented. But, these systems need to be proactively built. Moreover, for this to bear fruits, governments' should accelerate efforts of building an integrated financial ecosystem.

See the Innovation-centered ecosystem Blockchain is nurturing Today in Finance Technological changes have metamorphosed the banking industry in unimagined ways. One of the newest disruptors – Blockchain – lets multiple participants to fact-check data without allowing them access to the details of the data. Sounds impossible? Well, the Blockchain has rightly earned its status as a disruptor. In a Blockchain, information is validated by multiple contributors, making it impractical, and almost impossible to hack or manipulate. The information is not readable by its validators and can be accessed only by those who own it. Worthy of praise by the biggest tech. giants, Blockchains are thriving as a savior for the data world. Its job may sound like an upgraded version of a bank’s major task – security for sensitive data. And, it’s precisely that! Thanks to the new-age storage, banks now have one less task. Overcoming the skepticism of its initial days, Blockchain is doing what it promised. Major industries have finally accepted that the safekeeping and legitimization of data are much easier than ever before, which was once cost-and-labor intensive. Though on the flip side, the widespread adoption of Blockchain has given rise to a furthermore disruptive sentiment – Decentralized Finance. Decentralized Finance: Can we Safely Call it the Future of Finance? Decentralized Finance (DeFi) is a Blockchain-based form of finance that does not rely on central financial intermediaries such as brokerages, exchanges, or banks to offer traditional financial instruments, and instead utilizes smart contracts on blockchains. DeFi services run on open-source software code, just like Blockchain, and are the most user-inclusive financial medium to date. According to academicians, DeFi products and services can be combined and modified in endless ways, but are prone to major risks too. In May 2021, over $80 billion worth of cryptocurrencies were locked in DeFi contracts, up from less than $1 billion a year earlier. On Aug 3, 2021, the total value of the market was $69 billion. Though, it’s only a fraction of the $20 trillion global financial sector. A huge wave is headed our way, and banks need to observe shifting sentiments to mitigate risks. Appropriately, Blockchain offers new-age data storage and security to help banks cut costs and shift their focus to the need of the hour. Mainly, adopting Blockchains based on integrated solution architecture will allow banks to set the ball rolling for advanced adoptions in the future. Secondly, competing in the digital sphere needs modernized customer-centricity for a varying range of services, and Blockchains have been developed solely for ease of use and simplicity in user protection. On that note, here’s what Banks should look forward to strategizing services for – Adopting Blockchain Today? Look to Build an Integrated Finance Ecosystem The shift that Blockchain is heading towards is – Advanced cognitive systems and AI, ML, and NLP capabilities will soon access Blockchains and help in evolving the customer experience. It should help create cohesive and personal digital journeys by harnessing a data environment renovated with structured, unstructured, alternate, and internal data – all accessible easier than ever. Open banks and other innovative financial services are being built with Blockchain in their foundation, which is posing a challenge for banks with a traditional customer base. Banks are in cold waters as they need to retain customers while building a new-age ecosystem that will only come from tireless innovation with quick tech. adoptions, rounded integration strategies, and an evolved outlook towards banking. Here, even though Blockchain-based banking is in its infancy, a few integrated ecosystems are already being adopted by Global and Indian Financial Institutions. The research for these developments in India is being led by Institute for Development and Research in Banking Technology (IDRBT), let's take a look – Single Digital Identity Verification Banks wouldn’t be able to carry out online financial transactions without identity verification. However, the verification process consists of many different steps that consumers tend to dislike. For security reasons, all of these steps need to be taken for every new service provider, but with Blockchain, processes are shortened extensively. 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. No-Bounds Global Trade Finance Trade finance, where all financial activities are related to international trade and commerce, is still dependent on invoices, letters of credit, and bills. Multiple order management systems pushing to get the work done online are failing because the process takes a lot of time. Blockchain-based trade finance will streamline trading processes and get rid of time-consuming manual processes, eliminate paperwork, and integrate important processes using the most secured channels. Next-Gen Security for Quicker-than-Ever Finance Companies are evolving their customer-centricity by following data-centric approaches and are transforming customer experience for it to work as a competitive differentiator. Apart from the main issue of security in the integration of huge ecosystems, Blockchain will help with - Faster payments with publicly visible legibility of recipients Faster settlements and money transfers without lengthy processes of regulation Faster buying and selling of assets under a single ledger that connects practically everything Simpler fundraising through legally protected public trading platforms Get in touch with D2K Banking Fintech Consultancy Experts for more information on roadmaps for deploying new-age technologies for comprehensive analytics services.

As Bad loans pile up again let's revisit the role of cash-flow audits before lending to MSMEs With the bad loans piling up again, cash-flow audits before lending to MSMEs make more sense than ever before. On the contrary, the growth of MSMEs narrates the overall growth story of the world economy. MSMEs represent more than 90% of all business establishments around the globe, and as per the estimates, additional funding of $5.2 trillion would be required for MSMEs to grow. This makes reliable Loan Origination Systems (LOS) a notable component in all financial organizations. What makes Loan Origination Systems (LOS) important? Credit Analysis through a LOS determines the risk associated with the entity and tags a risk rating. Diving deeper, a credit analysis allows lenders to estimate the Probability of Default (PD), Exposure at Default (EAD), and other important parameters to assess the negative impact it may have in the future. Simply put, LOS as a system helps financial organizations assess the risk involved in approving credit to a particular entity. This helps in avoiding bad loans in the future. To take a step further, lenders can now use LOS systems to accurately picture cash flow analysis, trend analysis, and ratio analysis. These parameters help calculate the risk involved in the journey. When coupled with Early Warning Systems (EWS), LOS helps to detect loan defaults at very early stages. These factors consistently ensure that benchmarking MSMEs loans play a vital role for banks and allow them to maintain a healthy loan portfolio. LOS and the 5 Cs of Commercial Credit Analysis In an advanced LOS system, the entire process goes through a series of checkpoints, with applications getting rejected at any interval. These metrics and parameters vary with the banks. However, here are the 5 Cs that every commercial credit assessment processes: Character While this may sound unusual but the character does play a vital role in loan origination. Most banks dig previous and current reputations to understand the nature and the characteristics of the business. Some of the factors that enable banks to assess the business are credentials, past credit reports, and reputation in the industry. The background verification ensures that the company lends to better eligible businesses. Capacity The next very crucial aspect of credit analysis is capacity. Capacity refers to the monetary aspect of an organization. This involves paying expenses, present cash flow, credit score, and other important factors like current debts. If the banks find that a business is hardly meeting its expenses, in most scenarios the bank won’t lend the sum. Capital Capital refers to investment made by the core team into the business. A higher capital value represents the seriousness of the founders and is often perceived as a positive sign. This helps banks move forward in the LOS cycle. Collateral Collateral is the security against the loan. It could be an equal or higher value asset that ensures that the bank holds the right to sell it and recover the default value. Collateral could reduce the risk factor greatly and, hence, is preferred by banks. Conditions Finally, the application ends with terms and conditions of repayment. Conditions are mutually accepted and ensure a projected track of repayment. This also ensures established customer categorization and risk profiling from the bank’s end. Concluding Remarks Being the largest chunk in the business ecosystem, MSMEs need capital to sustain and grow. However, it is due diligence from the bank’s end to verify all the information and take a calculated risk. This is where a reliable LOS comes in place and cannot be ignored at all costs.

See How Digital Lending Platforms Enrich Strategies with End-to-end Customer Journey Data Without the right data sources and analytics capabilities, pre-qualification can stretch for days. This can be devastating when dealing with emergencies and can badly affect fund-driven businesses. Several initiatives and changes in banks’ policies have narrowed down the hassles. More importantly, the wider adoption of critical lending solutions has transformed how Banking was previously perceived. So What is a Digital Lending Solution? What is CRisMac Loan Origination Solution? Before we dive into the details of Digital Lending Solutions, we need to understand the gaps it addresses. A Loan Application goes through a series of checkpoints before finally being granted or rejected. The application can get rejected at any stage, making it cumbersome for Banks to track hundreds of thousands of applications at any point in time. CRisMac Loan Origination Solution manages the entire loan dispersal cycle. Our solution's features are customizable for different loans. For instance, the requirements of a mortgage differ vastly from a personal loan, but the backend data processes control the flux. How CRisMac Digital Lending Solution Performs at Different Stages of Loan Origination The stages of loan origination can be categorized into several broad aspects. However, the initial stages play a crucial role in user experience. The CRisMac Digital Lending Solution works as a dynamic data aggregation, data analytics, and rule-based decisioning engine. The standardized stages and the role of our solution in loan origination are: Pre-qualification Lending Interfaces Application Processing Underwriting Credit Decisions Pre-qualification The first step of the loan origination process is the pre-screening step. The borrower reveals their requirements to a Bank and submits crucial details to verify their authenticity. CRisMac Digital Lending Solution sources information from multiple credit bureaus and alternative data sources. Digital Lending Interfaces The next step is the application stage. In this stage, the applicant shares relevant information such as any existing loans, assets, and business functionalities to the banks. CRisMac Digital Lending Solution easily integrates with apps, websites, and digital portals including BaaS platforms to secure data and build data transfer pipelines for customers' declarations and sensitive data. Application Processing and Underwriting Post submission of details, the Banks go through a critical stage where the information is verified, and the decision on the application is made. Further, the Bank decides how much amount could be allocated using different scoring mechanisms such as credit scores, outstanding loans, risk scores, etc. CRisMac Digital Lending Solution offers a well-built information flow and comprehensive dashboards for customer profiling and sharing information to different verticals. Credit Decisions The result of underwriting is the credit decision. In this stage, the bank decides whether to loan or not. Again, automated, AI/ML-powered solutions simplify the entire process within a few minutes. CRisMac Digital Lending Solution runs Intelligent Decisioning based on advanced Statistical Analytics Solutions (SaS). Get in touch with D2K Banking Fintech Consultancy Experts for more information on roadmaps for comprehensive data analytics in digital lending.

Here's a Deep Dive into the Indian Banking Sector to Understand the Importance of EWS "Gross NPAs of public sector banks doubled in last seven years, SBI tops the list," the title published in Times of India went viral soon it went online on December 16, 2021. As per the reports, the public sector banks had a cumulative figure of Rs. 5.40 lakh crore in 2021 – a massive hike or a double from Rs. 2.24 lakh crore in 2014. These figures raise red flags about the Indian banking systems and their risk assessment philosophies. This is where a structured system, an EWS System, comes in place. So What Exactly Is EWS And How Does It Function? EWS stands for Early Warning Signals. The system assesses a loan application on a critical number of factors and provides a report. This, in turn, helps banks decide whether to lend or not. To take a step further, the system supplements the practice of due diligence in loan disbursal and credit defaults that primarily constituted the contributing factors to many frauds. These systems also use artificial intelligence and double down their effectiveness with the help of advanced algorithms that make them dependable. Following are the utilities of an EWS System in financial ecosystems. Utilities of an EWS in the Financial Ecosystem Securitization of Assets Securitization of assets has always been a matter of conflict for banks. To a certain extent and without the aid of automated systems, banks lose sight of the loans and hence need systematic assistance. With EWS in place, banks get suggestions about bad loans that need immediate attention. For example, post several warnings if an individual is unable to clear dues, or an agreement is inconclusive, the bank owns right to sell the assets to recover investments. EWSs identify such cases in real-time and suggest steps to prevent bad loans. Reducing Probability of Customer Defaults A majority of signs of loan defaults are visible at the initial stages themselves. Thus, these factors can be assessed in infancy while saving the banks from a bad loan. However, a majority of loans default in mid-way as well. Legacy systems fail to identify these gaps, and hence the bad loans rise dramatically. EWS system uses advanced indicators and triggers that identify bad loans in real-time and hence cannot be ignored at all costs. Identification of Opportunities and Business Patterns AI-based EWS can further track businesses that are performing in their industries. This gives them insights into sectors that are investible and aid them in finding better opportunities. This goes a step forward from systems that identify bad loans only. Conclusion While there are innumerable ways an EWS system can assist the banks, these are the system's core functionalities. The AI-driven EWS system from the house of D2K Technologies is one of the brightest examples of how things operate in major banks of India. These solutions outline every possible parameter and scenario and hence, cannot be ignored at all costs.

‘Automation is good, so long as you know exactly where to put the machine.’ - Eliyahu Goldratt, Author, and Business Philosopher The global financial crisis (2007-2008) was disastrous for a majority of professionals in banking and finance. As the sector recovered, automation software helped save time on operational tasks. Gradually coming to be known as Robotic Process Automation (RPA), it grew to eliminate almost all 'repetitive tasks'. As technologies evolved, one question that surfaced was 'How can automation get smarter?' Enter: Artificial Intelligence (AI). Teamed with RPA, it automates complex data-intensive tasks. Notably, RPA and AI can be accommodated in existing infrastructure without a full sweep. By thinking big, starting small, and scaling fast, most financial entities could make use of contemporary technology rollouts with fewer roadblocks. Intelligent Automation: Wide-reaching AI Capabilities for Large Scale Applications The fusion of RPA and AI – termed intelligent automation (IA) looks promising for accelerating processes. According to a Forrester report, intelligent automation will release $ 134 billion in labor value in 2022. So, how does IA work? To begin, let's look at its components and functions. Artificial Intelligence (AI) utilizes information gathered from various sources and feeds that information to tools used for Robotic Process Automation (RPA). The framework is mostly the same for all IA-based processes. This merger which forms Intelligent Automation (IA) majorly enables automation at larger scales and accelerates complex workflows. It creates solutions using a technological knowledge base to streamline interactions between multiple, otherwise disconnected applications. Majorly today, IA is used for - Intelligent document processing - Extract, validate, and process unstructured data using AI tools like Natural Language Processing (NLP) Process discovery - Create complete guides for automation in RPA-based processes Streamlined workflows - Automate interconnected workflows to increase efficiency As seen in today’s banking and finance sector, the duo is iconic in tackling problems at large scales. To unchain IA's true potential, developments are based on considerable research. Consequentially, high costs for its development and integration demand that Financial Entities consider all possible use cases while drafting blueprints for its inclusion. Intelligent Automation in Banking: Complete Overhaul of Backend and Data Processes Covering an ocean of opportunities by building seamless experiences in automating end-to-end operational work, IA has reached new heights. In all major industries, IA is capable of automating almost all back-office and data management tasks. In the banking sector, IA marked its presence by enabling banks to easily carry out automated data sourcing, data verification, data processing, complex analytics, and reporting. The benefits of Intelligent Automation (IA) for Banks and Financial Entities are - Minimized Operational Costs Technology is in its golden era. The potential for operational cost savings is colossal. As here, advanced interoperability for any banking activity helps with cost-efficiency. RPA extracts data and collates information at large scales, and AI addresses difficulties in data cleansing and aids in the creation of reports for comprehensive insights. Apart from delivering a massive overview of workflows, the duo makes it possible to – Automate hyper-personalized customer interaction processes at every level with dataflows for real-time data Build databases and highlight customer behavior predictions in segments like collections and marketing IA-Enhanced Vigilance It is often difficult for banks to trace all the transactions flagged for possible fraud. Although, RPA can track transactions and raise red flags for possible fraud transaction patterns in real-time. In an interconnected systems network, AI is used for advanced know-your-customer (KYC) processes and new-age Anti Money Laundering (AML) safety procedures. In the mentioned cases, with RPA, the capabilities of AI such as machine learning, deep learning, speech recognition, natural language processing (NLP), and visual recognition, highly enhance audit abilities and real-time traceability respectively. Risk Supervision and Reporting RPA can be used to trail full audits and other pivotal processes for mitigating risks and maintaining compliance. RPA in risk management can be primarily seen in the areas of risk monitoring, risk control, and risk reporting. In tandem, AI quickly spots patterns in large and unstructured datasets. This has a huge potential to enhance the speed and accuracy of crime detection. It also works to automate and enhance data-intensive activities in regulatory reporting, thereby lowering risks whilst reducing costs. For Risk Reporting, AI makes it possible to build comprehensive and detailed risk analytics. Its ability to add variables to the analysis process, and to compute at unreal speeds improves risk calculating systems exponentially. Get in touch with D2K Banking Fintech Consultancy Experts for more information on roadmaps for deploying technologies for comprehensive data analytics.

See how Banks and NBFCs compare to Digital-first Financiers in the race toward Digitalization Neobanks are innovation powerhouses for customer experience, which has helped transform Global Banking. With everyone going digital at an ever-accelerating pace, neobanks have a huge window for innovation in diverse segments. One major segment for expansion consists of Gen-Z. And, then there are other exquisite niche consumers that have new age demands. In India, neobanks are still tasked with having to carefully gauge changes in regulatory compliance. To date, brick-and-mortar banks have the upper hand because their regulations are spelled out to the tee. Naturally, neobanks have no choice but to cater to the same segment that their counterparts have dominated for years. As digitization intensifies, neobanks could soon lead the way. Though still, the current scenario is a rare instance for industry leaders to be toe-to-toe and still learn a thing or two about innovating at a faster pace. Neobanks v/s Digital Banks: Better Analysis of Data is the Tie-breaker It’s no surprise that data plays the biggest role in tiebreakers for the bigger piece of the pie. The better existing data is understood; the better these players get at 'customer acquisition'. On the other hand, 'customer retention' is a bit complex. When it comes to brand loyalty and better customer relations, data needs to be looked at differently. A major question asked here is what data are banks allowed to capture and how can banks maximize insights. Indian Neobanks’ priority is to proactively identify and build new products and services that customers want. Products backed by strong research and favoring regulations may get them to the door, but building platforms that genuinely enable new-age insights and risk supervision is a primary requirement. For digital banks, offering customizations for a niche consumer base comes with the responsibility to better your services as you progress. Though, the question is similar – do our everyday banks have systems that will help them understand data cyclones brewing over new-age market segments? New Age Analytics Services Level the Playing Field India has never avoided taking striking decisions and is most talked-about for its reforms to drive the Fintech industry forward. UPI by NPCI - National Payments Corporation of India, for instance, received acclaim worldwide because of which many countries, such as UAE, are pursuing the model ambitiously. Similarly, Indian regulators have called for the adoption of technologies that have put both players at par – Advanced AI/ML-Driven Management Information Systems In India, one major hurdle for neobanks is that new regulations have forced their offline counterparts to catch up in the digital sphere. RBI's mandates for end-to-end report automation have given Banks’ management the roadmaps to ramp up systems and make use of reports of superior quality. Major business decisions like increasing customer centricity and transforming an outlook towards major assets are now possible with revamped analytics systems. In an attempt to strengthen systems, data lakes are being implemented using cloud storage for easy, seamless data processing and AI/ML-driven intelligent automation. While staying ahead in the tech-frenzy atmosphere, Neobanks needs to safely model analytics systems around current regulatory requirements and also account for future amendments. Meanwhile, Banks and NBFCs need to pay close attention to insights into customer behavior to continue to stay on top of the game. 360° Credit Monitoring Systems for Diverse Customers Credit offerings are changing at an increasingly fast pace. Line of Credit (LoC) is majorly favored by various MSME borrowers, and it is working really well as a credit solution. With new credit solutions becoming mainstream, banks are burdened with new tasks for comprehensive risk analysis. Back in the day, banks could only pick up patterns inferred from first-party data, though now RBI has allowed the implementation of models that track early warning indicators from alternative data sources too. Scalable technologies are needed to cover more data and cutting-edge Machine Learning (ML) algorithms are needed to derive insights. As neobanks enter the corporate sector, superiority in consumer behavior analytics will be the game-changer for both players. Currently, Financiers can analyze customer behavior trends with data from Transactional, Financial, and Alternative sources. Though, keeping a watch out for new trends and possibilities is the only way either one can improve. Conclusion: The Real Differentiator Banks can leapfrog technology adoption with emerging tools. They can build technology ecosystems to improve product development life cycles, enhance digital connect for customer satisfaction, and increase efficiency in internal operations. When well architectured, these boost profitability in the long run. Ultimately, the real winner will be decided not by the speed of their journey toward digital transformation, but by the readiness of their teams to proactively adopt the changes headed our way. Get in touch with D2K Banking Fintech Consultancy Experts for more information on roadmaps for deploying new-age technologies for comprehensive analytics services.

Explore Newly Prescribed Data Sources for Advanced Analytics and Timely Reporting Data is the new oil, which makes analytics the driving force for harnessing its power. But, access to resources trumps all. ‘Data Subscriptions’ are an unmissable asset. Where access to High-quality Data is setting businesses apart from contenders, it is being used to help customers like never before. In today’s financial scenario, banking functions like fund management, loan allocation, and successful recovery are the tip of the iceberg. As the digitized world is connected on more fronts, and hence trustworthy relationships with your customer/clients do not suffice. In using High-quality Data, a business relationship in the Finance world can be nurtured into a value-adding affiliation. For a new-age bank, some critical areas requiring High-quality Data include modernized communications for building a community, customer-retention strategies, and the ever-increasingly crucial task of risk aversion. It may seem like a tough road to go down without the right resources, read: the right Data Subscriptions. Data is Integral to the Merger of Problem Discovery and Solutioning Today, quick and agile data processing is pivotal to the ever-growing repository of data, more commonly known as big data. When analytics processes are built well, banks can stand out with unique efforts, especially for today's digital consumers. Data Subscriptions play a huge role in analytics, for relevant, clean, and optimized data. Since regulators have stepped in to clarify the heat around the rules for access to big data sources, Banks are profiting in multiple scenarios. Multiple cases where banks have made strides in utilizing data subscriptions are – Predictive Analytics for Unparalleled Risk Aversion Early Warning Systems (EWS) offers data analytics that helps banks and financial institutions predict defaults long before they happen. Recently mandated by regulators, the systems are proving to be a boon in lending. It majorly helps with successful recoveries. Customer Analytics to Ace Trends for Digital Customers Credit Monitoring Systems (CMS) offers data analytics to understand better its customer base, product performance, and market trends. Unlike predictive-financial analytics' quicker results, insight generation for widespread marketing strategies offer benefits in the long run. Accelerate Analytics Processes with Data that Matters Good data is data you can collect, clean/enrich/transform, and make insightful, with the sole goal of improving decision-making. In a transparent digital world, data could be structured, unstructured, and semi-structured. Adhering to standards, ‘good data’ is what data subscriptions offer. The good data coming in from data subscriptions are pre-empted for specific decision-making. Moreover, it helps business leaders with the best inputs from the dreaded big data ocean. According to regulators, Which Data Subscriptions are Important to Banks Today? We offer data subscriptions for Know-your-customer (KYC) information validated by the Customer Information Commission (CIC), Financials and Reported Earnings submitted by businesses, Alternative Data, Environmental, Social, and Governance (ESG) Data, Litigation-related Information, and more. Data for Predictive Analytics for Corporate/MSME Lending Financials, Annual Report, Details of Company, Associates, Directors, Charge, etc. from MCA (Ministry of Corporate Affairs). External Ratings from CRISIL, CARE, ICRA, India Ratings, Brickworks, Acuite, etc. News, tweet - 2500+ sites - Domestic, International, Business-related. Willful Defaulters, Shell Companies, Default Companies & Directors from MCA, SEBI, Bank, and RBI sites. Litigation details - NCLT, NCLAT, High Court, and Supreme Court. Tax Defaults, PF Defaults, etc. from the related sites. Data for Trends, Technological Inclusions, and Marketing Strategies Customer Behaviour and Trending Market Sentiment Regulatory Norms and Information on New-inclusions Segment-specific Data on Clients and Industries Regulator Approved Customer/ Company Third-party Data D2K Technologies’ Analytics systems source finance-specific business data for stakeholders and business leaders as well as for regulatory requirements such as – IRAC Norms | IGAAP Reports | Ind AS Reports. Get in touch with D2K Banking Fintech Consultancy Experts for more information on roadmaps for deploying new-age technologies for comprehensive analytics services.

Data integration for regulatory reports improves Banks’ approach to Data Centralization For the past few years, there has been a drastic change to Indian Banking. Right from the rise of customer-centric banking products to the inclusion of new payment modes, more and more activities aim to improve customer experience, promote financial inclusivity, and increase informed lending. Because of this shift, its dependency on technologies has increased, directly impacting the number of digital services and data generation. Subsequently, Banks need to manage their assets and liabilities better. In doing so, they can proactively adopt data systems that align with future goals. In digitalization, regulators see scope for huge improvements in risk monitoring. Instead of just being responsible for customers’ misdoings after the deed is done, a comprehensive approach to data-first banking helps with proactive risk management. Let's how policies that promote digitalization like the mandate for Automated Data Flow (ADF) benefit Banks in the long run. Automated Data Flow: One Answer to All Banking Reporting Woes RBI introduced the initiative of Automated Data Flow (ADF) in 2010. The Reserve Bank of India's (RBI) approach paper outlines a framework for putting in place a Central Data Repository (CDR) with various layers for data acquisition, data integration, data conversion, data validation, and data submission. Since there is heavy diversification of systems, Banks require software experts that can be either third-party vendors or in-house IT teams. The data used in ADF reporting also works for detailed research reports such as Ind AS, IGAAP, and more. Consequently, the process of centralizing data offers Banks a boost for internal data requirements such as cleansing, democratization, and research. Considering these benefits, Banks can preplan their data goals while implementing Automated Data Flow (ADF) method. CRisMac ADF: Build a Centralized Data Repository for Regulatory Reporting and More Even with deadlines in place many banks are yet to become ADF compliant. For this, D2K Technologies offers an out-of-the-box product based on its 20+ years of research in Banking Technology. Our product CRisMac ADF is created by banking technology consultancy experts, specializing in a comprehensive approach to data. Our new age data-flow model is an enterprise-wide solution that implements the prescribed Centralized Data Repository (CDR) concept. Our R&D for the product is a continuous process based on the expertise gained while building the solution for several Banks and a host of successful ADF implementations. CDR is the main component facilitating internal reporting, as well as regulatory reporting for RBI, Ministry of Commerce, SEBI, and other regulatory requirements. Here, data is principally sourced from Core Banking Systems (CBS) and peripheral transaction systems like Asset Classification, Treasury, External Data Providers, CRM Tools etc. The solution also accommodates manual entries through Gap Data screens for missed/recalculated data. It is equipped with MOC screens for changes suggested by auditors. With these features, a proper audit log is maintained for auditors and internal supervisors. Why Choose CRisMac ADF to Automate Banking Reports? In a nutshell, the implementation of Automated Data Flow (ADF) is designed to make the process of reporting easier and quicker. With minimal manual intervention, the reports would be in line with banks' financial statements ensuring consistency and comparability. Many Banks are investing considerably in the ADF method, but consulting experienced techno-functional bankers helps improve efficiency and prepare for oncoming disruptions. Our Banking Technology consultancy experts are a team of dedicated former banking professionals who have worked on enhancing banking technologies for 20+ years. Today, products created by D2K Technologies are active in 8 out of 10 top banks in India. Get in touch with D2K Banking Fintech Consultancy Experts for more information on roadmaps for deploying technologies for comprehensive data analytics.