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How Does Automated Data Integration and Reporting help Banks Explore a Data-first Approach? Over the years, the Indian banking industry has been transforming constantly. The Reserve Bank of India (RBI) has mandated various new technological upgrades. This is mainly to help Banks to improve the management of assets and liabilities. Notably, this has made Banks better at framing strategies for corporate vision and goals. One such mandate, Automated data flow (ADF) ensures that banks conduct businesses fairly, transparently, and responsibly. Automated Regulatory Reporting offers regulators better offsite supervision. It improves FIs' data integration efforts and accelerates data sharing from lending systems using a straight-through reporting process (STP). While improving the regulator's data visibility, the method benefits both decision-makers too. The comprehensive reporting method ensures that Banks meet current-day reporting standards. Additionally, it allows the same reports to provide well-structured data for business strategy. Automated Data Flow (ADF) Solutions equip Core Banking Systems with modern data capabilities. The step to mandate it is a strategic move for a technology-driven data collection approach, aiming to achieve 100% automation for data flow. Which Banking Reports Can be Automated Today? Banks have to submit a set of 222+ regulatory reports at varied frequencies to the RBI. These reports fall under 12 categories: Financial Statements Analysis Basic Statistical Returns Department of Banking Supervision (DBS) Returns Analysis Foreign Exchange and International Operations Statutory Returns Analysis Risk Management Delinquency and Collections Treasury Reconciliation Fraud Advances Deposits Regulatory Climate in India | The Past and the Future Currently, the reporting process within the existing scenario is complex and most Banks need time to smoothen the process. This is mainly because the task of modernizing legacy banking systems comes with the added responsibility to create a comprehensive data-first environment for purposes like internal reporting, credit monitoring, market research, and effective supervision. Formerly, Banks submitted regulatory reports manually. It raised issues with audits as the end product had inefficient and uncleaned data. As per the ADF mandate, RBI asked Banks to create a central data repository (CDR) that works as a data warehouse harboring granular data from every banking data source system. As the data is stored as per data definitions provided by regulators, the same data also suffices for internal insights making it important for Banks to look into every possibility to utilize the centralized data. The first stage requires timely solution deployment, seamless integration of a CDR, and maintenance of data cleansing processes. For the second stage, Banks need robust internal corporate governance specifically for regulatory reporting. Internal audit processes should be tightened, avoiding third-party vendors from handling the maintenance of ADF once the automation exercise is completed. An internal ADF maintenance team should be in the picture that handles the dynamic nature of reporting requirements. In recognizing technology upgradation as a process that helps RBI, it is expected of them to issue clear and exhaustive guidelines for all regulatory reports. They are required to establish defined channels and processes for member Banks to deal with queries on reporting. The regulator is also expected to be more flexible in terms of timeframes for automation of regulatory filings. Get in touch with D2K Banking Fintech Consultancy Experts for more information on roadmaps for deploying technologies for comprehensive data analytics.

New solutions for credit monitoring enable a wider outlook for identification of potential defaulters In today’s fast-emerging world, Banks and Financial Institutions (FIs) are making strides in innovation. The combination of Robotic Process Automation (RPA), AI/ML-powered systems, and statistical models enable a comprehensive outlook on data in Digital Lending. These technologies maintain process performance and transparency to enhance efficiency in lending. Where the spotlight shifts to a new approach in Banking – 'Value Co-Creation’, these are beginning to play a bigger role. The approach helps Banks base consumer engagement on products and services co-created with active inputs from borrowers. Most of the offerings in the approach personalize customer journeys, where technologies simplify functions for the intended engagement. In line, collections from Special Mentioned Accounts (SMAs) are becoming easier with Value Co-creation’s clear goals in place – to guide borrowers in times of distress. Aided by systems that run predictive analytics, Banks can face problems with this new age outlook. Value Co-Creation in Lending: Shifting from Customer-centric to Customer-driven Nowadays, people are inherently creative and want to shape their own experiences. Here value co-creation comes into play. Engaging and collaborative activity with Banks helps create a well-developed feedback loop. In the case of repayments, this helps Banks enable a truly customer-driven outlook. For collections processes, engaging borrowers is a difficult but important task. Adopting the value co-creation approach requires lenders to maximize positive interactions. Engaging interaction improves after-sales teams in an attempt to be considerate towards a borrower's hardships. Here, data solutions using new-age technologies help lenders proactively solve customers' problems in situations where an account shows incipient stress. For this, systems monitor many factors of a borrower's account and enable a holistic view of any changes in repayment terms if needed. Recent research shows, Banks using this approach deliver value two to four times greater than those which don’t leverage the co-creation business model. This value gap has four main tenets: increased organizational flexibility, improved customer insight, greater revenue growth, and lower marginal costs. Traditional Ways to Identify SMAs and What's New? Special Mention Accounts (SMAs) are those assets/accounts that show symptoms of bad asset quality in the first 90 days or before it is identified as NPA. Identifying these accounts is necessary for early discovery of stress in bank loans. Special Mention Accounts are usually categorized in terms of duration. However, these accounts exhibit signs of irregularities indicating the possibilities of more stress in coming days. These can easily be tracked in in financial statements. Some discrepancies that is noticed in accounts are — delays in submission of stock or financial statements, payment defaults, delays in repayment, etc. Gradually, regulators are expanding the list of tracking indicators that tag an account as stressed. New-age credit monitoring systems go beyond the traditional methods of identifying accounts. The traditional method of tagging stressed accounts are — SMA 0 are accounts where the Principal or Interest payment is overdue for less than 30 days, but show signs of incipient stress. Here the case can also be recognized as SMA-NF which refers to Non-financial (NF) signals of incipient stress. This can be because of other factors reflecting potential sickness /irregularities in accounts. SMA 1 refers to those loan accounts in which the installment or interest is overdue for 1 month from 31 day to 60 days. SMA 2 refers to accounts in which the installment or interest is overdue for 2 months from 61 days to 90 days These are called 'Early Warning Signals (EWS)' in banking parlance. These are valid indicators for stress in a borrowal account. These are intended to alert the management that if no corrective / appropriate action is initiated on the SMAs well in time, then such accounts may turn bad and become NPAs. Before big data came into play, incipient stress was identified in a few borrower activities like frequent return of cheques issued by borrowers, non-payment of bills discounted or under the collection, incomplete documentation in terms of creation / registration of charge / mortgage etc., and more. Early Warning Signals, today, are now collated from transactional (based in CBS), Financials (Financial Submissions), external media (social, news), backward and forward industry linkages, and other statistical data from analytics solutions. Early Warning Systems: Big Data and Predictive Analytics for Credit Monitoring Built to identify early warning signals, an Early Warning System (EWS) powered by Artificial Intelligence (AI) and Machine Learning (ML) predicts and prepares Banks for risks from borrowal accounts 9 - 12 months in advance. Apart from the traditional signs of stress, it enables lenders to track external causes and their effects before it manifests in loan books. For internal as well as external causes, Big Data Analytics (BDA) collects, organizes, processes, and analyzes large, diverse (structured and unstructured), and complex sets of data. Further on, predictive analytics helps predict future events mainly referring to specific types of analysis on current activities, market standing, and backward-forward industry linkages. Proactive Engagement with Borrowers: Foreseeing Opportunities in Challenges Newer monitoring processes bring transparency and help minimize risks well before time. More importantly, these new-age credit monitoring systems help improve customer experience with foresight into portfolio performance. Value Co-creation can be prioritized and potential defaulters can be engaged in more interactions during a repayment tenure. However, the deteriorated loan portfolios which are on the verge of falling into delinquency can be administered by a One Time Settlement (OTS) solution, which offers intelligent decisioning to zero in on an amount that can be demanded from defaulting borrowers.

Systems for the management of non-performing assets help allocate time, efforts, and investments for better recoveries The digital age has simplified getting a loan with online payouts and reliable loan management services. But, for lenders collections remain a challenge. For unpaid loans termed as non-performing assets (NPAs), the Reserve Bank of India (RBI) has prescribed One-time Settlements (OTS) for relief to both borrowers and Banks. In this day and age, timely repayments are key for banks to grow. For this, proactive efforts towards defaulters help in monitoring settlements for credit, liquidity, and operational risks. Here, Recovery and Collection software boosts the maximum value from settled payments by enabling faster collaboration and timely measures. Banks that still use manual processes to settle payments are burdened by risks arising in data collection, transaction monitoring, and post-reconciliation adjustments. Moreover, one-time settlement solutions integrate with credit monitoring systems to optimize efficiency for repayments. CRisMac OTS: One-time Settlements Synced with Credit Monitoring Solutions Settlements are a time-consuming process as banks have to calculate the due amount based on the insights of a borrower, and schemes of RBI, and these have to be accurate. A comprehensive monitoring and repayment calculation solution is a catalyst in the process. CRisMac OTS solution manages written-off debts (partial/full) on settlements of all claims (if any) or as an internal arrangement. It helps streamline recovery operational efforts that are a huge cost where the yield cannot be predicted. In-built Recovery Analytics optimizes the costs of these recoveries by offering Statistics-based calculations using ML-powered solutions. On monitoring datasets of borrower behavior, the solution offers an overview of each portfolio and customer segment. It eases the process of portfolio monitoring with automation for insights and in-depth analysis allowing relationship managers to act on time-sensitive developments. Moreover, transactions can be monitored easily with accuracy checks for data and content validity, interchanged qualifications, balancing errors, and rejected values. How Do OTS Solutions Account for Risks and Increase Profits? Banks have to process and settle payments from different partners depending on the loan or the payment chain. Generally, they end up with different formats of the same transaction records at the end of a settlement cycle. CRisMac OTS helps avoid manual intervention done to match or reconcile the amounts across disparate sources. Our solution simplifies timely reconciliation, challenges in calculations for auditors, and reduces the high costs of running a back office. Furthermore, Banks can drive business growth using settlement data in near real-time to deliver value-added services to borrowers. The profits are a result of a few factors — Efficiently account for liquidity in assets Speedy and prompt recovery of loans and advances Get ample time to identify unusual behavior and take preventive measures Quickly Adopt an OTS Solution for any Core Banking or Digital Lending System A new-age solution for settlements offers API-first, cloud-backed digital technology. Banks can upgrade existing systems with zero downtime. It enables accurate monitoring with seamless data delivery to responsible teams, along with multi-dimensional views and analysis of data. CRisMac OTS integrates fully with legacy, on-premise, or cloud-based systems. Get in touch with D2K Banking Fintech Consultancy Experts for more information on roadmaps for deploying technologies for comprehensive data analytics.

Banks can cross-collaborate better on delinquencies with real-time updates on borrower activity A Bank's ability to generate profits depends not only on its assets’ quality but also on the dynamics of data management for its impaired assets. In June 2022, personal credit rose at an annual rate of 18 percent, double the percentage points (nine percent) from July 2020 before the peak of the COVID-19 pandemic. Moreover, non-performing assets (NPAs), and bad loans in the Indian banking system soared after the Reserve Bank of India conducted an asset quality review (AQR) in 2016, and finally stabilized in 2022 at 5.9 percent, the lowest in six years. In the current scenario, an RBI report has stated Banks’ gross NPAs may climb to 8.3 percent by March 2023 from 5.9 percent in March 2022 in case of severe stress. As the volume increases for such assets locked up in litigation, improved processes for monitoring are the need of the hour. With information scattered around, it is difficult to keep a record of every case. Integrated Litigation Management offers dashboards for an overview of debtors, data accessibility for litigation teams, and information sharing using reports and notifications. CRisMac ILMS | Integrated Legal Management System Collaborate Effectively and Streamline Recovery Efforts for Quicker Settlements CRisMac Integrated Litigation Management System (ILMS) is a solution for Lending Entities to streamline recovery efforts of legal processes. CRisMac ILMS is a data management solution offering real-time auto-syncing of information updated by various teams involved in recovery and litigation. New age recovery efforts require updates from field agents but can use data from internal analytics systems too. CRisMac ILMS is a comprehensive solution that has provisions for seamless system-to-system dataflow, generation of insights, and automation of major tasks. Integrated with Asset Classification Systems Banks and lenders need to keep record of every borrower, and continuously monitor whether they will be a liability or an asset. A borrower’s account is classified as a non-performing asset (NPA) if the repayment is overdue by 90 days. In such cases, the lender has to initiate recovery action immediately. CRisMac ILMS automates data sourcing for NPAs from credit monitoring or asset classification systems. An ‘NPA Status Note’ for borrowers can also be generated apart from the wide range of other reports which touch upon various aspects of the recovery process. Status notes can also be viewed on a mobile app allowing Banks’ Executives to take quick decisions. Automated Notice Generation for Defaulters The system generates important notices for borrowers – Default Notices, Recall Notices, SARFAESI Demand Notices, possession notices, Sale notices, and certificates of sale. It stores complete case details like Case Descriptions, Courts, Titles, Opposition parties, and attorneys, related documents etc. to increase information-sharing amongst stakeholders. One Solution for All Recovery Litigation Efforts Litigation Monitoring Solutions help Banks take timely actions for a variety of legal matters in lending. Some of the recovery actions covered in CRisMac ILMS are – Securitization and Reconstruction of Financial Assets Enforcement of Security Interest Act (SARFAESI) Act – Physical Possession, RP Fixation, Sale Notice, Auction, Sale through Private Treaty, SARFAESI Movement Chart Debts Recovery Tribunals (DRT) and DRATs Suit Filed Cases - Pre, post, and during details - Insolvency and Bankruptcy Code (IBC) - National Company Law Tribunal (NCLT) - National Company Law Appellate Tribunal (NCLAT) - Supreme Court - Liquidation - Committee of Creditors (COC) meetings Performance and Development Review (PDR) Act Consumer Complaints extracted and recorded (Suit Filing) Account for related areas such as arbitration Cases filed under section 138 of the Negotiable Instrument Act Section 25 of the Payment and Settlements System act Banking Ombudsman Right to Information (RTI) which was adopted in 2005 Litigation Against the Bank Criminal Cases against the Bank/Officials Get in touch with D2K Banking Fintech Consultancy Experts for roadmaps to deploy comprehensive data analytics solutions.

AI/ML-powered Credit Monitoring Systems rely on regulator prescribed EWS methodologies Across both the public and private sectors in India, banks are yet to overcome the challenge of bad loans. In the financial year 2021, public sector banks in India reported a total of over INR 6 trillion in gross non-performing assets (NPA). This was a decrease from INR 7.3 trillion Indian rupees in 2019. In contrast, private sector banks reported an increase from INR 1.8 trillion in FY 2019 to INR 2 trillion in FY 2021 in gross NPAs. The slight improvement of the banking stability indicator (BSI) in September 2018 was due to improved asset quality, but it had also been noticed that profitability has eroded further. Further, the banking sector also observed a delay in detecting and reporting such frauds to the regulators. Due to such occurrences, the banking sector has been under pressure from regulators to adopt technology-driven proactive measures to tackle the menace of NPAs and loan frauds. A Revolutionary Approach to NPAs: Early Warning Systems Keeping frauds in mind, the RBI proposed Early Warning Systems (EWS) to depict beforehand, whether accounts are becoming NPAs or not. In support of this thought, D2K Technologies offers a befitting EWS product. CRisMac Early Warning System: A set of automated processes for identifying risk at a nascent stage in the bank's loan portfolio. The rule-based solution identifies borrowers at risk of distress or default well before they actually default. A host of Early Warning Signals are identified from data of systems and processes already implemented in banks. Early warning signals are categorized into – transactional, financial, non-financial, external, and statistical indicators – to identify emerging problems in credit exposure at an early stage. These consist of real-time business data from internal transaction activities, submitted financials, and big data from external factors such as forward-and-backward sector linkages, ESG data, news, social media, and more. The automated rule-based EWS identifies the traces of potential stress/default long before the occurrence of actual default, enabling the bank to initiate timely remedial measures. Hence, EWS facilitates the bank in strengthening the health of the bank’s credit portfolio.

RPA-powered systems are enabling game-changing strategies for sustainability in volatile markets In this day and age, banking digital infrastructure is at new heights. Right from sanctioning loans to predicting money laundering, it’s all possible digitally. What’s more? These processes generate data that enhance marketing decisions. A management information system (MIS) carries out data processing in real-time. Financial Entities can now monitor borrowers’ assets and generate insights for monthly as well as periodical decision-making. Automated analysis helps with quick lending and corrective actions against defaulters. Its comprehensive approach to reporting offers varied insights for business decisions. These systems intensively automate all aspects of reporting to a Bank’s management as well as enhance scope for automation of regulatory reports. Capabilities of Management Information Systems for Banks? The solution compiles data from multiple lending systems that are also required for regulatory submissions. It generally uses a centralized data repository (CDR) and automatically transforms data for Automated Data Flow (ADF) format, prescribed by RBI. It hosts a robust analytics system for Asset Classification as prescribed in RBI guidelines. However, for easy access of data to c-suite executives and stakeholders these asset classification reports are also generated on a monthly basis. These reports offer users the capability to draw meaningful insights regarding assets. To ensure smooth flow of high-quality data in a timely manner, it is essential that – ● Uniform data reporting standards are developed internally ● Data flow is automated from the source systems of banks to their MIS server ● Data is automatically submitted to regulators without any manual intervention Trends for MIS Systems Simplifying Insight Generation Automation for Data Collection Automated Data Collection extracts data from analog sources using AI/ML-powered solutions. Automation reduces all tedious, mundane work from backend processes without human intervention. Algorithms for fully-automated data collection frees your workforce for decision-centered job roles. The volumes of data grow non-stop, which is great news for businesses. However, it gets more complicated and expensive as new data accumulates. Automated data-collection processes increase productivity and cut costs, all while enhancing data delivery. Automation of Information Analysis Automated information analysis also known as intelligent decisioning, can assist in important decisions on behalf of enterprise stakeholders and create useful feedback mechanisms. One example here, the MIS analytics system constantly runs a study and generates data visualizations. An employee's time is more valuable, especially when it comes to data analysis. By automating tasks that don’t involve a high degree of human ingenuity or imagination, employees can focus on uncovering new insights to guide data-driven decisions. Cloud Implementation for Streamlined Data Flow Cloud implementation has grown considerably as a Software as a Service (SaaS) solution. It breaks down data silos, improves connectivity and visibility, and ultimately helps automate business processes by easing data-access. It is a response to the dire need to unify information components in Banks. Easy accessibility to data stored in a cloud improves operational efficiency, increases flexibility and scalability, and offers a competitive edge with real-time data for automated collection and analysis.

Banks using Early Warning Systems are a step closer to unlocking the potential of Advanced Analytics In the financial year 2021, the Reserve Bank of India (RBI) reported bank frauds amounting to INR 1.38 trillion. A lack of due diligence by lenders is one major contributing factor to losses from credit accounts and fraud. Here, an Early Warning System (EWS) driven by Artificial Intelligence (AI) and Machine Learning (ML) predicts, prevents, and prepares lenders for credit risk. Apart from constant monitoring of a company's transactions and reported financials, the system enables lenders to track external causes of stress and its effects on accounts before it manifests in loan books. How Do Early Warning Systems Prepare Banks for Loan Defaults? A predictive analytics feature analyses innumerable operational events during the entire lending cycle. EWS analyses different patterns during borrowers’ tenures and uses big data analytics to generate rich, deeper, and meaningful reports on borrower behavior. The system is based on the concept of Early Warning Signals. These signals are generated based on a number of triggers computed using statistical models, mainly emanating from - transactional, financial, non-financial, external (alternative), and statistical data. Here's how. Enables Big Data Monitoring for Predictive Analytics Big Data Analytics (BDA) collects, organizes, processes, and analyses large, diverse (structured and unstructured) and complex sets of data. It helps build systems for 'Predictive Analytics' to predict future events using data on a borrower's current activities, market standing, and backward-forward linkages. These bring transparency and help minimize risks, but more importantly help improve user experience with foresight into portfolio performance. Based on the banking insights, the analytics process, including the deployment and use of BDA tools, improves operational efficiency. This strategic potential lets Banks and Financial Institutions (FIs) explore new revenue streams and gain a competitive advantage. Monitors Regulator-prescribed Borrower Activity Financial Entities conduct an in-depth analysis of a borrower’s past behavior to get an insight and assess the level of risk involved in the loan process. However, this has become obsolete as business-related internal and external factors of borrower's accounts can now be monitored. When EWS is in place, accurate data for Forward-and-Backward linkages are also monitored. The system provides a backward industry and forward industry impact analysis that creates multiple visualizations and insightful reports for a holistic credit evaluation. Automated 24x7x365 Borrower Account Monitoring The solution helps analyze financial statements in real-time, by aggregating all the business stakeholders' account details. To streamline credit evaluation, the solution automatically identifies and classifies different kinds of transactions, running in-built potential fraud detection checks. This results in a precise report about inflows and outflows and the average monthly balance, among other things. The framework also identifies monthly, weekly, and quarterly patterns of transactions made by the borrower through the individual's company bank accounts. Uses Alternative Data - News Alerts and Social Media Detailed borrower-wise EWS reports are available and an executive summary is provided to know the gist of the cases. For a comprehensive outlook on borrower activity, the system also sources data from multiple alternative data sources that include external public data, news, tweets, social media activity, etc. Interactive dashboards are available in the EWS system to view borrower-wise details. Heat maps on risk categorization on all borrowers are provided to easily identify the ones that required immediate attention.

New-age credit monitoring systems improve monitoring for all types of borrower accounts Traditionally, loans are sanctioned based on available data on borrower behavior. Banks would determine companies’ qualifications for Line of Credit (LOC) on three factors — the maturity of the business, the involved collateral, and the capacity limit - the capability of repaying the borrowed amount, and collateral. But now, both regulators demand better, analytics-driven measures. Regulators suggest, lending should be supported by modern day credit monitoring and risk mitigation data systems. These analytics systems, today, offer insights on potential for an account to default. The ask extends to involve line of credit (LOC) borrowers too. Inclusion of Big Data: Helping Monitor Credit Lines Better The fundamental advantage of line of credit (LOC) is its high flexibility. Borrowers can customize LOC expenditure to their particular needs, paying small amounts of interest on the amount borrowed instead of the entire credit line. Borrowers can also change their payments based on their budget and cash flow. Most banks in India still use a compliance-driven factoring mechanism for lending. On the other hand, modern digital lenders are simplifying borrowers' repayment journeys with proactive account monitoring. Prescribed by regulators, newer guidelines for account monitoring give lenders the option to factor in available news and social media accounts too. Here, predictive analytics, a major function of early warning systems (EWS), helps lenders with timely decisions for potential defaulters. Since LOCs are dynamic lending models, AI/ML-powered EWS solutions help companies source and process data quickly, from extensive systems for banking transactions made by the borrower and external data. What is Helping Banks Lean On Advanced Analytics? An in-depth analysis of a borrower’s past behavior can help predict only to a limit. With big data at the forefront, data analytics can help predict borrower behavior in real-time. Along with market performance, trigger-based red flags for borrowers’ internal and operational activity help banks stay one step ahead of accounts showing incipient stress. Here's how — Client’s Real-time Financial Data Lenders can analyze real-time data that contributes to risk. The most important being — Transactional Triggers – Early warning signals embedded in and emanating from a borrower's transactions recorded in a lender’s systems. Financial Triggers – Financial information of borrowers normally not residing within the transactions recorded in bank systems. Sources: Certified borrower submissions, specialized data agencies, Registrar of Companies, Eqifax, etc. External Triggers – based on market sentiments on the borrower, Group, Industry, Regulatory changes etc. Statistical Triggers – considers the statistical analysis of borrowers' historical data, past performance, industry performance, etc. Going forward, predictions for potential defaults are based on statistical computations for Probability of Default (PD), Distance to Default (D2D), and Loss Given Default (LGD). Highly Informative Data-Visualization Multi-purpose, interactive dashboards of EWS solutions offer a detailed view of borrowers. Teams get access to heat maps for risk categorization on all borrowers and can easily identify the ones that require immediate attention. Detailed borrower wise EWS reports are made available and executive summaries can easily be created for cases. Furthermore, as data stored in the system increases, Machine Learning (ML) modules for statistical computations improve in accuracy for predictions. Quick Deployment of Advanced Analytics Software as a Service (SaaS) deployment through cloud implementation breaks down data silos, improves team connectivity, and increases data visibility. An EWS solution integrates data through cloud-based applications and automates the generation of actionable insights. When implementing a proactive credit risk reporting system like EWS, one significant step is implementation of cloud-based systems. Apart from quick deployment, it improves data access, tracking, and comprehensive reporting by offering multiple teams easy access to all borrower data.

Digital Lending has benefits enhanced by a data-first outlook and modernized systems For many years now, a number of financial startups have successfully set up digital lending platforms. In some cases, digital-only fronts are the sole force driving lending. Where digital lending is a threat to Traditional Banks, quick digitization can safeguard them. An end-to-end digital experience, low costs on transactions, and in-app tools helping to mold financial habits are huge value additions to new-age consumers. Banks need to sustain customer-centric digital experiences at new scales. Especially, where Artificial Intelligence (AI), Blockchain, and Cloud technologies will soon make up a majority of Banking infrastructure. A flexible and scalable platform for digital lending could result in raising loan volumes while reducing operational costs, improving underwriting, and lowering fraud rates too. What Readies Banks for New Age Digital Lending? Before bringing Big Data Analytics to the forefront, it is necessary for banks to harness the power of a truly transformative data outlook – Centralized Data. Using Data Warehouses for Centralized Data, Banks edge closer toward automation for loan generation. Banks can highlight common data points such as credit score, household income, and demographics, and create in-house data-driven processes for loan sanctioning. Furthermore, Centralized Data transforms infrastructure to promote automated regulatory reporting and data-first credit monitoring to introduce predictive analytics for credit risk. Benefits of Digital Lending Systems for Banks Today Loan origination can be automated with digital lending solutions, saving valuable time and redirecting human resources from redundant tasks of data entry and verification. Data aggregation can be automated for relationship managers (RMs) to access relevant data and bring risk-monitoring scores to their fingertips. Data cleansing can be automated with AI-ML-powered systems. Data Synchronization can keep redundant data out of the way. And, data subscriptions can deliver time-sensitive data to analysts, researchers, and c-suites. Faster Digital Lending with Online Applications Online lending interfaces and applications are offered by every bank today. It saves time and helps with better decisions, based on the amount of information shared by the prospect. These interfaces can help borrowers access credit information sourced directly from external Credit Information Companies (CICs), helping lenders sanction loans quicker and reduce turnaround time. Automated Data Collection for Pre-qualification Data Automation helps in streamlining and provides reliable and consistent dataflow for any stage of the loan origination process such as Credit Analysis, Credit Presentation, Portfolio Risk Analysis, Decision, and Approval. Data automation accelerates the lending process while offering improved reporting and audit reports. Reliable Underwriting with Intelligent Decisioning Manual Credit Underwriting processes are being replaced by digital underwriting not only because of optimized access to borrower data. AI/ML-powered intelligent decisioning is a driving factor too. On analyzing multiple aspects of a potential borrower including alternative data, new big-data-enabled lending solutions speed up approvals while providing a quicker and more reliable lending environment. Easy Data Integrations using Cloud Technology Cloud technology is making services available to a wider range of clients, simplifying applications, and improving connectivity between multiple systems and data sources. A few inclusions that Banks are increasingly considering for easier digital lending are cloud-based Banking as a Service (BaaS) solutions to increase co-lending and include newer credit enablement options. On the data front, the benefits of cloud-based loan management data integration are undisputed. Lower capital and operational costs, easier data integration, and seamless data delivery to multiple decision-making teams. Optimized Activity Tracking and Reporting Once a borrower receives the funds, loan origination ends. However, the next process of credit monitoring requires data and borrower information to be formatted, stored safely, and transferred seamlessly. In order to achieve this, the tracking and reporting solutions should have easy access to lending data. By storing and accessing Centralized Data, solutions improve the data flow of borrower information. It integrates with solutions for asset classification and credit monitoring. The information stored can be easily accessed for loan monitoring, budgeting, and accounting. Get in touch with D2K Banking Fintech Consultancy Experts for more information on roadmaps for comprehensive data analytics in digital lending.

Learn about new age benefits of value co-creation in digital lending, Explore the potential to reshape interaction with borrowers New age digital banking demands that lenders adopt a customer-centric approach. Moreover, value is evaluated differently today. Community engagement is preferred, and personalized services are now a necessity. For digital transformation, recognizing such trends helps Businesses adopt key solutions to stay at the forefront of innovation. In current times, a unique consumer trend that’s running the show is ‘Value Co-creation'. Value Co-creation: A Shift in Value for Modern Customers Value Co-creation is a collaborative activity where consumers have the freedom to avail self-customization for products and services on digital platforms. The trend promotes active participation in different product or service propositions through apps and websites. Ultimately, this helps businesses develop 'co-created' marketing strategies, and aids in activation of customer-centric initiatives and offerings. If done right, a well-developed feedback loop helps businesses to delve deeper into customer prioritization. It is safe to say that ‘value creation’ that was the core of good business strategy has transformed into 'co-creation of value between customers and businesses'. The same goes for Banks and other Financial entities in Lending. Swift adoption of the right digital systems is key for banks and FIs to build a collaborative approach and utilize this trend to its benefit. Two Approaches for Value Co-creation in Lending Studies explore the various new-age benefits of value co-creation, especially its potential to reshape interaction with internal and external stakeholders. As banks become more customer-oriented for newer innovations and gain stronger competitive positions, customer involvement and satisfaction have become tenets for the process of digital transformation. In digital lending, access to new functionalities, concern and a caring attitude, prompt customer service, error-free bank services, and transparency are important factors of experience contributing to the co-creation process. Provide Self-Customizations and Study Customer Decisions The internet has changed the process of lending. Before applying for a loan, Banks are being scrutinized as much as their loan offers. To eliminate customer hassles, dependable online aggregators help customers zero in on the cheapest loans. Where aggregators are not involved, banks are able to engage customers through online portals and customize their loan offerings to meet specific needs. In choosing either one, a customer allows lenders to understand his/her requirements while customizing their loan for themselves. Value co-creation-prioritizing delivery models can be built into chatbots, loan disbursal interaction models, or banks can plainly use software that lets customers choose the loans that fit their demands. Apart from offering choices, Banks’ marketing teams can hugely benefit from the data coming in from interactions with the apps and websites. Scores of data can be used to build new product offerings, and to better understand customer segments. Interact Empathetically to Build Value-based Relationships Customer information analytics and comprehensive report generation help in radically rethinking business models and changing the markets Banks serve. A shift towards adopting the value co-creation approach requires Finance providers to maximize positive interactions with customers and reconsider an optimal mix of communication channels. Here, analytics assists in reassigning staff to the borrowers who are positively influenced by a phone call, email, SMS, or letter from collections teams. Interaction with aftersales and collection teams is transforming into a process that considers customers’ hardships. As a result, customers in collections are no longer seen as irredeemably bad credits and greater incentives are being introduced to maintain brand loyalty. The approach promotes the idea that when a customer is in better financial health, they will consider continuing with the lender. The Data that helps with this forward outlook is — Understanding Reasons for hardship: Unemployed, furlough/reduced income, medical, quarantined and more. Offering Monetary relief: Payment holiday, reduced installments, foreclosure, and more. Understanding borrowers' industries and developing customized strategies for new repayment modes. This process is dynamic and banks implementing the right systems simplify strategy building. Since two-way communication is key in understanding customers' circumstances, those falling into delinquency also opt for one-time settlements, to avoid hassles.

Management Information Systems are Being Transformed for Timely, Simplified Access to Comprehensive Data and Insights As the strategic outlook for Banking evolves, the role of managers extends beyond the roles of traditional management. To participate in strategizing for business, managers at every level need comprehensive data at their fingertips. However, getting the right data, in the right format, and at the right time is not an easy task. Management Information Systems (MIS), once used majorly for revenue and expense reporting, leaves other teams data-starved today. Now, fueled by simplified solutions for modernization, new age MIS solutions are transforming the way banks approach data analytics. Mainly, deployment of these systems aid in democratization of data by using data lakes and AI/ML-automation, making data accessible to key stakeholders in the form of detailed reports. 'Democratization' increases the scope of data usage with automated reports for C-Suites, and offers access as and when the need arises. Building Data-first MIS Systems Requires Strategic System Integration As the banking sector is digitizing , laying the groundwork for futuristic capabilities is pivotal. Comprehensive feedback from current operations, at every level, helps develop time-critical business strategies. Here, system data from cross-functional teams plays a key role. Even now, integrated systems are missing from the Legacy System Architecture which is still powering data processes of major Financial Entities. Legacy systems, today, can majorly be transformed with cloud-based solutions. Banks can now deploy systems quicker, increase scalability, and magnify analytical capabilities cost efficiently. Moreover, where replacing existing systems attract unneeded costs, cloud-based solutions help map system upgradations/integrations to immediate business objectives. In improving data sourcing and accessibility, new age MIS Systems rightly promote – Quick Access: Single Data Depository, Data Lake, or Data Mart Today, implementing a data lake is like renting a storage locker; stuff gradually builds up, and businesses may end up paying for storing unnecessary data. And, yet avoiding it isn’t an option because of the insurmountable uses of data integration. To optimize data-usage, banks need to create data management strategies and consider data-specific use cases beforehand. Current-day MIS systems excel at utilizing the availability of large quantities of coherent data along with deep learning algorithms to recognize items of interest. Ultimately, the goal for a single data depository is faster-than-ever data access and real-time decision analytics. Regulatory Reports Sourced from Comprehensive Company Insights Since, reporting automation solutions for Automated Data Flow (ADF) already regulate data quality and consistency, coupling the same data for the creation of MIS reports is a new-generation industry practice. Internal reporting requires 'dynamic visualization' to understand liquidity, profitability, credit risk, and fair value. Moreover, large exposures, solvency, and leverage are factors that need to be closely monitored. To offer dynamism, modern Banking MIS solutions allow data to be showcased in a number of graphical representations, and allow for these to be easily shared via phones, increasing accessibility of reports. All this while delivering quality insights for regulatory reporting too. Simplification of Data Sharing and Collaboration for Reports Along with automating Data Flow to regulators, data needs to be transformed into insights for extensive reports such as Ind AS and IGAAP. Multiple teams collaborate for these reports, which is only possible after sharing data extensively across teams. Modern MIS solutions allow managers/supervisors to control data quality of submitted regulatory returns, collect key insights quickly, and explain concerns to stakeholders with detailed reports. Features for MIS solutions helps tackle the sheer complexity of a number of modern-day strategic reports – Visual Insights as well as balance sheets for complex topics Multiple data quality checks at every stage of processing Easy addition of new data quality checks for different reporting User Acceptance Testing (UAT) of regulatory reporting production D2K Technologies' Management Information Systems Solution offers banks wholistic system integration for comprehensive reports. The system's data and insights serve multiple purposes including Automated Data Flow (ADF) for Regulatory Reporting.

Newer Analytics Systems are helping Banks identify market opportunities and close gaps with time-sensitive NBFC, and FinTech collaborations Buy now, pay later (BNPL) is the fastest-growing 'online payment method' in India, predicted to rise to 9% for all online payments by 2024. Banks turning a blind eye to its rapid adoption are undermining the competition, as well as opportunities, it brings to the credit space. New-to-credit customers are the payment method's biggest market, with other age groups increasingly buying in on the idea. The biggest driver is that BNPL offers lower-than-ever interest rates, mostly closer to nil. In the credit space, BNPL is an option that offers credit without the taboo surrounding debt. To understand other drivers, investigating evolving customer behavior is key. Data-intensive customer engagement, here, helps Banks hold their ground in fierce digital environments. Banks are on the Greener Side of Regulatory Compliance Buy now, pay later (BNPL) service providers have partnered with NBFCs to extend credit to consumers. Most BNPL services are offered by Fintech companies yet to face the scrutiny of regulators. The amount of debt they issue and to whom is still unregulated. As seen in recent times, unregulated institutions issuing debt expose themselves to more risk than their regulated counterparts. Many are viewed as entities providing tech-enabled services, and are absent from the ‘payment system operators’ list released by the RBI, dated 5 November 2019. Furthermore, some buy-now-pay-later programs have bad reputations which affects the service's image negatively. Here, traditional banks have seized a golden opportunity. They offer similar BNPL programs under the trusted name they have built over all these years. What Should Banks Consider about Buy now, pay later? As digitization intensifies, customers expect banks to be omnipresent. This allows newer entrants to enjoy niche segments, while, traditional banks are tasked with competing in digital environments. On a positive note, even in changing times, Banks are still trusted for wealth consultancy, protection of assets and much more. This trust creates a space for traditional names to focus on offering their clients a hassle-free journey into digital territory. When it comes to buy-now-pay-later, the payment option may look like one that hampers profits rolling in from credit cards. But, as financial markets explore the benefits of offering choices to customers, new areas of study show that both payment methods co-exist and complement each other. The option can be made available to anyone with a good credit score, leaving credit cards to function as a symbol of exclusivity. For both current and future customers, it is important to have the right offering in the right place when the need arises. Here, data-intensive customer engagement is the only option for lenders to make the most of the ever-growing segment. Analytics Gage Potential for New Markets and Customers In India, ICICI Bank recently entered a partnership with payment service provider Pine Labs to offer in-store pay-later in the retail space. Clients could make high-value purchases with payments split for monthly paybacks in installments. Globally, Challenger bank launched a credit card that mirrors BNPL services. It combined monthly charges into installment plans to let customers choose a repayment period between 24 and 60 months to pay it off. Banks can monitor such scenarios for 'utility bill payment history' and 'cash-flow analysis'. In the case of BNPL, banks leveraging CRM analytics can study how and why consumers opt for the service. New Age Analytics for Insights to Enhance Marketing Strategies Whether digital or not, lending processes are expected to prevent clients from taking on bad debt. Once lenders adhere to the guideline, safer transactions for borrowers, customers, and merchants come to be recognized as the lender's priorities. Data-intensive customer engagement helps with just that. New age ADF and MIS Systems provide integrated data, offer an enterprise-wide view of assets, client behavior, and risks. Banks can now accelerate data-to-information processes to equip major marketing decisions with valuable insights. Risk-Evaluating Analytics for Accurate Evaluation of Customer Segments As new Fintech services gain widespread adaptability, cutting-edge solutions simplify things further. BNPL may be a new-and-improved service, but for Banks it is an opportunity to offer choice, which is an unchallenged parameter for better customer engagement. Especially, at major brand contact points where credit billings can be replaced. In this scenario, banks can pit technology against technology and put in place risk-based systems to handle detailed quantification of customer segments. Risk-calculating market intelligence eliminates the worry around defaults, even from new-age credit offerings.