Glossary

Explore key terms and concepts related to AI and financial automation at GLIB.ai.

  • Artificial Intelligence
    Description
    Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think, learn, and solve problems like humans. In the context of BFSI (Banking, Financial Services, and Insurance), AI encompasses technologies that enable systems to perform tasks typically requiring human intelligence, such as recognizing patterns in financial data, understanding natural language, making complex decisions (e.g., loan approvals), and detecting fraud. AI in finance drives automation, enhances predictive analytics, and delivers personalized customer experiences.
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    • WealthTech
      Description
      WealthTech is a portmanteau of 'wealth' and 'technology,' referring to the application of innovative technologies, including Artificial Intelligence (AI), Machine Learning (ML), big data, and automation, to enhance and automate wealth management, financial planning, and investment advisory services. WealthTech solutions aim to democratize access to sophisticated financial advice, optimize portfolios, streamline operations for wealth managers, and deliver hyper-personalized client experiences.
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      • BFSI
        Description
        BFSI stands for Banking, Financial Services, and Insurance. It is a broad acronym encompassing all sectors within the financial industry. This includes commercial banks, investment banks, non-banking financial companies (NBFCs), insurance providers (life, health, general), asset management companies (AMCs), wealth management firms, and various other financial intermediaries. AI and digital transformation are rapidly reshaping operations across the entire BFSI landscape, from front-office customer engagement to back-office processing and compliance.
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        • Document Processing
          Description
          Document Processing refers to the set of operations involved in handling, extracting, and organizing information from various types of documents, both physical and digital. In the BFSI sector, this often involves high volumes of unstructured or semi-structured documents like bank statements, KYC forms, loan applications, invoices, and insurance claims. Modern document processing leverages AI and automation to achieve greater speed, accuracy, and efficiency compared to traditional manual methods.
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          • Bank Statement Analyzer
            Description
            A Bank Statement Analyzer is an AI-powered software solution designed to automatically extract, categorize, and analyze financial data from bank statements. Utilizing technologies like OCR (Optical Character Recognition) and NLP (Natural Language Processing), it processes statements in various formats (e.g., PDF, scanned images) to identify income sources, expenditures, cash flow patterns, EMI payments, and potential anomalies. In lending, it provides instant borrower risk assessment, improves credit underwriting accuracy, and detects fraud.
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            • Intelligent Document Processing
              Description
              Intelligent Document Processing (IDP) is an advanced form of document processing that uses Artificial Intelligence (AI) technologies such as Machine Learning (ML), Natural Language Processing (NLP), and Computer Vision (CV) to understand, classify, extract, and validate data from documents. Unlike traditional OCR, IDP can handle unstructured and semi-structured documents, learn from new document types, and adapt to variations, significantly automating workflows in areas like loan origination, claims processing, and accounts payable by converting raw document data into actionable insights.
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              • Robotic Process Automation
                Description
                Robotic Process Automation (RPA) is a technology that uses software robots ('bots') to automate repetitive, rule-based tasks typically performed by humans. These bots interact with existing systems and applications just like a human would, often without requiring changes to underlying IT infrastructure. In BFSI, RPA is used to streamline workflows such as data entry, reconciliation, report generation, and basic customer service, working in conjunction with AI and IDP to enhance efficiency in back-office operations and accelerate processes like loan approvals.
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                • Machine Learning
                  Description
                  Machine Learning (ML) is a subset of Artificial Intelligence (AI) that enables systems to learn from data without being explicitly programmed. ML algorithms identify patterns, make predictions, and improve their performance over time as they are exposed to more data. In the BFSI sector, ML is fundamental for tasks such as credit scoring, fraud detection, predictive analytics for market trends, personalized investment recommendations, and optimizing operational efficiencies through data-driven insights.
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                  • Natural Language Processing
                    Description
                    Natural Language Processing (NLP) is a field of Artificial Intelligence (AI) that focuses on enabling computers to understand, interpret, and generate human language. In financial services, NLP is crucial for analyzing unstructured text data found in documents, emails, customer interactions, and market news. It helps in extracting relevant information from legal documents, summarizing client communications, powering chatbots, and identifying sentiment for investment decisions or risk assessment.
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                    • Optical Character Recognition
                      Description
                      Optical Character Recognition (OCR) is a technology that converts different types of documents, such as scanned paper documents, PDF files, or images captured by a digital camera, into editable and searchable data. While traditional OCR primarily focuses on converting images of text into text, advanced OCR (often combined with AI) can extract data from complex layouts and varying fonts, serving as the foundational layer for Intelligent Document Processing (IDP) in the BFSI sector to digitize physical and digital documents.
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                      • Human-in-the-Loop
                        Description
                        Human-in-the-Loop (HILT) is an AI model or system that requires human intervention or oversight at certain points to either train the model, validate its outputs, or handle exceptions that the AI cannot confidently resolve. In Intelligent Document Processing (IDP) for BFSI, HILT ensures high accuracy by flagging uncertain extractions or anomalous cases for human review, allowing for continuous learning and adaptation of the AI system, thereby improving overall system performance and building trust in automated processes.
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                        • Turnaround Time
                          Description
                          Turnaround Time (TAT) refers to the total time taken to complete a process or fulfill a request, from initiation to completion. In the BFSI sector, TAT is a critical Key Performance Indicator (KPI) for processes like loan approvals, insurance claims processing, or customer onboarding. AI and automation solutions, like those offered by Glib.ai, are designed to significantly reduce TAT, leading to faster service delivery, improved customer satisfaction, and enhanced operational efficiency.
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                          • Average Handling Time
                            Description
                            Average Handling Time (AHT) is a contact center or operational metric that measures the average duration of a single transaction or task, from the start of the interaction/task to its completion. In manual document processing within BFSI, AHT can be extensive due to repetitive data entry, verification, and cross-referencing. AI-driven automation aims to drastically reduce AHT by streamlining workflows, thereby freeing up human resources for more complex or strategic activities.
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                            • KYC
                              Description
                              KYC stands for Know Your Customer. It is a mandatory process for financial institutions to verify the identity of their clients. KYC regulations are designed to prevent financial crime, including money laundering, terrorist financing, and fraud. In the digital era, AI-powered KYC solutions automate identity verification through document analysis (e.g., PAN, Aadhaar, passport), biometric authentication (Face Matching, Liveness Detection), and cross-referencing with government databases, ensuring compliance and faster customer onboarding.
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                              • AML
                                Description
                                AML stands for Anti-Money Laundering. It refers to the set of laws, regulations, and procedures designed to prevent criminals from disguising illegally obtained funds as legitimate income. In the BFSI sector, AI plays a crucial role in AML by analyzing vast transactional data to identify suspicious patterns, flag high-risk activities, and automate Suspicious Transaction Reporting (STR), thereby enhancing fraud detection capabilities and strengthening compliance frameworks.
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                                • Fraud Detection
                                  Description
                                  Fraud Detection involves identifying and preventing deceptive activities carried out to gain financial or other benefits illegally. In the BFSI industry, AI and Machine Learning models are extensively used for real-time fraud detection. These models analyze vast datasets, transactional patterns, behavioral anomalies, and document authenticity (e.g., using AI to detect tampered bank statements or forged IDs) to flag suspicious activities and significantly reduce financial losses. Glib.ai's solutions, for instance, incorporate over 31 AI-driven fraud checks.
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                                  • Credit Scoring
                                    Description
                                    Credit Scoring is a statistical method used by lenders to assess the creditworthiness of loan applicants. It assigns a numerical score based on various factors such as credit history, income stability, repayment patterns, and existing debt. Modern credit scoring models leverage AI and Machine Learning to analyze a broader range of traditional and alternative data points (e.g., bank statement analysis, digital footprints) to provide more accurate, real-time risk assessments and facilitate faster, more inclusive lending decisions.
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                                    • Loan Origination
                                      Description
                                      Loan Origination refers to the comprehensive process a borrower goes through to apply for a new loan, from the initial application to the final disbursement of funds. It involves multiple steps including application intake, document collection and verification (KYC, income, property), credit assessment, underwriting, approval, and funding. AI-driven automation in loan origination significantly streamlines these steps, reducing manual effort, improving accuracy, and accelerating the overall turnaround time for loan approvals.
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                                      • Underwriting Automation
                                        Description
                                        Underwriting Automation involves using Artificial Intelligence (AI), Machine Learning (ML), and Robotic Process Automation (RPA) to automate parts or the entirety of the loan underwriting process. This includes automated data extraction from financial documents, real-time creditworthiness evaluation, risk assessment, and decision support based on predefined rules and learned patterns. Underwriting automation aims to accelerate loan approvals, reduce operational costs, and enhance the consistency and accuracy of lending decisions.
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                                        • Regulatory Compliance
                                          Description
                                          Regulatory Compliance in the BFSI sector refers to the adherence by financial institutions to laws, regulations, guidelines, and specifications relevant to their business operations. This includes directives from bodies like the RBI (Reserve Bank of India) and SEBI (Securities and Exchange Board of India), as well as international standards like AML and data protection laws. AI-powered RegTech solutions are increasingly used to automate compliance checks, monitor for breaches, generate audit reports, and ensure real-time adherence to evolving regulatory frameworks.
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                                          • Data Privacy
                                            Description
                                            Data Privacy refers to the protection of sensitive personal and financial information from unauthorized access, collection, use, or disclosure. In the BFSI industry, maintaining data privacy is critical due to the highly sensitive nature of customer data and strict regulations like India's Digital Personal Data Protection Act (DPDP Act, 2023) and GDPR. AI tools contribute to data privacy through features like PII Masking, robust encryption, access controls, and secure data handling protocols.
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                                            • Explainable AI
                                              Description
                                              Explainable AI (XAI) is a set of techniques and methodologies within Artificial Intelligence that aims to make the decisions and outputs of AI models transparent, interpretable, and understandable to humans. Unlike "black box" AI models, XAI provides insights into why an AI system arrived at a particular conclusion (e.g., a loan approval or denial). In regulated industries like BFSI, XAI is crucial for demonstrating fairness, mitigating bias, ensuring auditability, and building trust with both regulators and customers.
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                                              • RBI Master Circulars
                                                Description
                                                RBI Master Circulars (MCA), now often referred to as Master Directions, are comprehensive compilations of instructions, guidelines, and regulatory frameworks issued by the Reserve Bank of India (RBI) to banks and other financial institutions. They consolidate previous circulars and directions on specific topics (e.g., classification, valuation, and operation of investment portfolios; KYC norms; fraud risk management). For AI in WealthTech, adherence to the principles and specific mandates outlined in relevant MCAs is a fundamental compliance requirement.
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                                                • Table Extraction
                                                  Description
                                                  Table Extraction is an AI-powered capability within Intelligent Document Processing (IDP) that automatically identifies, parses, and extracts structured data from tables found in documents. This feature is crucial for financial documents like bank statements, invoices, and financial reports, where data is often presented in tabular format. It accurately recognizes rows, columns, and cell contents, converting them into a usable, machine-readable format (e.g., CSV, JSON), eliminating manual data entry and preserving data relationships.
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                                                  • QR Code Detection
                                                    Description
                                                    QR Code Detection is a feature that enables AI systems to identify and decode Quick Response (QR) codes present on documents or screens. In the BFSI context, QR codes are increasingly used for identity verification (e.g., on Aadhaar cards), digital payments, or document authentication. This feature allows for rapid data extraction and cross-validation, enhancing security, speeding up processes like digital onboarding, and providing an additional layer of automated document verification.
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                                                    • Signature Detection
                                                      Description
                                                      Signature Detection is an AI-driven capability that identifies the presence and location of handwritten signatures on digital documents. While primarily focused on confirming the existence of a signature, advanced systems can also flag anomalies or potential inconsistencies for human review. In financial workflows, it helps automate compliance checks to ensure all required documents are properly signed and authorized, reducing manual scrutiny and speeding up document validation processes.
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                                                      • Checkbox Detection
                                                        Description
                                                        Checkbox Detection is a specialized AI feature that accurately identifies the state of checkboxes (e.g., checked, unchecked, partially marked) on forms and documents. This capability is essential for automating the processing of applications, surveys, and declarations in the BFSI sector where user preferences or selections are often captured via checkboxes. It converts visual input into structured, machine-readable data, enhancing the efficiency and accuracy of data capture from forms.
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                                                        • Handwritten Text Recognition
                                                          Description
                                                          Handwritten Text Recognition (HTR) is an advanced AI technology that converts handwritten text from images or scanned documents into digital, editable text. Unlike traditional OCR which is optimized for printed text, HTR employs deep learning models to interpret diverse handwriting styles, variations, and complexities. In BFSI, HTR is invaluable for digitizing information from legacy documents, physical application forms, or notes, enabling comprehensive data capture and analysis where manual transcription would be time-consuming and error-prone.
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                                                          • Page Classification
                                                            Description
                                                            Page Classification is an AI-powered function that automatically identifies and categorizes individual pages within a multi-page document (e.g., a single PDF file containing various types of financial documents like a PAN card, bank statement, and salary slip). This capability is crucial for breaking down large, unstructured document bundles into their constituent parts, allowing each page type to be accurately routed to the appropriate specialized processing workflow (e.g., a bank statement analyzer or a KYC verification module).
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                                                            • Document Classification
                                                              Description
                                                              Document Classification is an Artificial Intelligence capability that automatically assigns predefined categories or labels to entire documents based on their content, layout, and other features. In the BFSI context, this means an AI system can distinguish between a loan application, an invoice, an insurance policy, or a regulatory report. This foundational IDP feature enables intelligent routing of documents, streamlines initial processing, and ensures that the correct automation workflows are triggered for each document type, improving overall operational efficiency.
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                                                              • Form Extraction
                                                                Description
                                                                Form Extraction is an AI-driven process that automates the capture of specific data fields from structured or semi-structured forms. Unlike general document extraction, form extraction is tailored to precise field locations and types (e.g., name, address, date, policy number, loan amount) on pre-designed forms such as loan applications, claim forms, or onboarding documents. It leverages AI models to accurately locate and extract data, reducing manual data entry, improving data quality, and accelerating form processing in BFSI.
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                                                                • Document Verification
                                                                  Description
                                                                  Document Verification involves using AI and other technologies to assess the authenticity, validity, and integrity of documents submitted by individuals or entities. In the BFSI sector, this is critical for KYC (Know Your Customer), fraud prevention, and compliance. It includes cross-referencing extracted data with external databases (e.g., government registries, credit bureaus), checking for consistency between multiple documents, and detecting signs of tampering or forgery, such as altered dates, mismatched fonts, or image manipulation.
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                                                                  • PII Masking
                                                                    Description
                                                                    PII Masking (Personally Identifiable Information Masking) is a data privacy technique that automatically identifies and obscures sensitive personal information within documents or datasets. This is crucial for BFSI firms to comply with data protection regulations like India's DPDP Act and GDPR. AI-powered PII masking ensures that sensitive data (e.g., bank account numbers, Aadhaar numbers, names, addresses) is redacted, anonymized, or tokenized before being processed or stored, minimizing data exposure risks while allowing for necessary data utility.
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                                                                    • Face Matching
                                                                      Description
                                                                      Face Matching is a biometric AI technology that compares a person's facial features from a live image or video against a reference image (e.g., from an ID document or an existing database) to confirm their identity. In the BFSI industry, Face Matching, often combined with liveness detection (to prevent spoofing), is used for secure digital onboarding (e.g., Video KYC), identity verification during transactions, and preventing impersonation fraud, providing a high level of security and a seamless user experience.
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                                                                      • Fintech
                                                                        Description
                                                                        Fintech (Financial Technology) refers to technology used to improve and automate the delivery and use of financial services. It includes a wide range of innovations from mobile banking and online payments to blockchain, AI, and big data applications in lending, investment, insurance, and wealth management. Fintech aims to make financial services more accessible, efficient, and personalized for consumers and businesses.
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                                                                        • Digital Transformation
                                                                          Description
                                                                          Digital Transformation is the process of adopting digital technology to fundamentally change how an organization operates and delivers value to customers. In the BFSI sector, this involves integrating AI, automation, cloud computing, and advanced analytics into core processes to improve efficiency, enhance customer experience, manage risk more effectively, and innovate new financial products and services, moving beyond traditional manual or legacy systems.
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                                                                          • Predictive Analytics
                                                                            Description
                                                                            Predictive Analytics is a branch of data analytics that uses historical data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes or behaviors. In BFSI, predictive analytics is used for forecasting market trends, assessing credit risk (e.g., predicting loan defaults), identifying potential fraud, predicting customer churn, and optimizing investment strategies, enabling financial institutions to make proactive, data-driven decisions.
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                                                                            • Generative AI
                                                                              Description
                                                                              Generative AI is a type of Artificial Intelligence that can produce new content, such as text, images, or other media, that resembles real-world data. Unlike analytical AI which interprets existing data, generative AI models (e.g., Large Language Models like GPT) learn patterns and structures from vast datasets to create novel outputs. In wealthtech, while still evolving, Generative AI could assist in drafting personalized financial plans, summarizing complex market research, or generating client communications.
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                                                                              • Neural Networks
                                                                                Description
                                                                                Neural Networks are a subset of Machine Learning and are at the core of Deep Learning. They are inspired by the structure and function of the human brain, consisting of interconnected "neurons" (nodes) organized in layers. These networks are highly effective at recognizing complex patterns in data, making them powerful for tasks like image recognition (e.g., for OCR and document analysis), natural language processing, and advanced fraud detection in the BFSI sector.
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                                                                                • Risk Management
                                                                                  Description
                                                                                  Risk Management in BFSI involves identifying, assessing, and mitigating financial risks. This includes credit risk (likelihood of borrower default), operational risk (failures in internal processes), market risk (fluctuations in financial markets), and compliance risk. AI plays a transformative role in modern risk management by providing real-time risk assessment, predictive analytics for early warning signals (EWS), automated monitoring of loan portfolios, and enhanced fraud detection, allowing financial institutions to make more informed and proactive decisions to safeguard assets and ensure stability.
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                                                                                  • Borrower Profile Enrichment
                                                                                    Description
                                                                                    Borrower Profile Enrichment is the process of building a comprehensive, 360-degree view of a loan applicant or existing borrower by aggregating, verifying, and analyzing data from multiple sources. AI-driven enrichment solutions pull information from bank statements, ITRs, GST returns, credit bureau reports, and even alternative data (e.g., utility payments, digital footprints). This enriched profile enables lenders to perform more accurate risk modeling, gain real-time financial health insights, and make precise creditworthiness predictions beyond traditional limited inputs.
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                                                                                    • Risk-Based Loan Pricing
                                                                                      Description
                                                                                      Risk-Based Loan Pricing is a lending strategy where the interest rate and loan terms offered to a borrower are determined by their assessed credit risk. Borrowers deemed higher risk receive higher interest rates, while lower-risk borrowers are offered more favorable terms. AI and Machine Learning models enhance risk-based pricing by providing more granular and accurate credit assessments, allowing lenders to optimize interest rates, manage portfolio risk effectively, and achieve a balanced risk-reward profile for their loan book.
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                                                                                      • P2P Lending
                                                                                        Description
                                                                                        P2P Lending (Peer-to-Peer Lending) is a method of debt financing that allows individuals to borrow and lend money directly to each other, often without the use of a traditional financial institution as an intermediary. Online platforms facilitate these transactions by connecting borrowers with investors. AI plays an increasing role in P2P lending platforms for automated credit scoring, fraud detection, and risk assessment to match borrowers and lenders more efficiently and mitigate potential default risks.
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                                                                                        • NBFC
                                                                                          Description
                                                                                          NBFC stands for Non-Banking Financial Company. These are financial institutions that provide banking services without holding a banking license. They are regulated by the Reserve Bank of India (RBI) but cannot accept demand deposits. NBFCs offer a wide range of financial products and services, including loans, credit facilities, investment products, and more. AI and Intelligent Document Processing (IDP) are crucial for NBFCs to streamline their high-volume lending operations, manage risks, and ensure regulatory compliance efficiently.
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                                                                                          • RegTech
                                                                                            Description
                                                                                            RegTech (Regulatory Technology) refers to the use of technology, particularly Artificial Intelligence, Machine Learning, and Big Data, to enhance and automate regulatory compliance processes within the financial industry. RegTech solutions help financial institutions comply with regulations more efficiently and effectively by automating tasks such as transaction monitoring, risk assessment, reporting, and identity verification (KYC/AML). This reduces the manual burden of compliance, minimizes human error, and ensures real-time adherence to evolving regulatory frameworks.
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                                                                                            • Digital Onboarding
                                                                                              Description
                                                                                              Digital Onboarding refers to the fully digital process of acquiring and integrating new customers without the need for physical paperwork or in-person interactions. In the BFSI sector, this involves using technologies like AI-powered document verification (for KYC documents), biometric authentication (Face Matching, Liveness Detection), and seamless data capture to allow customers to open accounts, apply for loans, or sign up for services entirely online. Digital onboarding significantly improves customer experience, reduces turnaround times, and lowers operational costs.
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                                                                                              • Model Risk Management
                                                                                                Description
                                                                                                Model Risk Management (MRM) is a comprehensive framework within financial institutions to identify, measure, monitor, and mitigate risks arising from the use of quantitative models, including AI and Machine Learning models. In BFSI, MRM ensures that AI models used for credit scoring, risk assessment, fraud detection, and investment strategies are accurate, reliable, fair, and compliant with regulatory expectations (e.g., RBI guidelines on AI ethics and explainability). It involves rigorous validation, continuous monitoring for model drift, and establishing clear governance structures.
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                                                                                                • Artificial Intelligence Governance
                                                                                                  Description
                                                                                                  Artificial Intelligence Governance refers to the framework of policies, rules, and procedures designed to ensure the ethical, responsible, and compliant development and deployment of AI systems. In the BFSI sector, AI governance addresses critical aspects such as algorithmic fairness, bias mitigation, data privacy, transparency (explainability), accountability for AI-driven decisions, and cybersecurity. Robust AI governance is essential to build trust in AI, manage associated risks, and ensure adherence to evolving regulatory mandates for financial institutions.
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                                                                                                  • Data Ingestion
                                                                                                    Description
                                                                                                    Data Ingestion is the process of collecting and importing data from various sources into a storage system or processing pipeline for further use. In the BFSI industry, data ingestion involves bringing in vast amounts of structured and unstructured data from diverse sources like internal databases, third-party APIs (e.g., credit bureaus), documents (via IDP), and real-time transaction feeds. Efficient data ingestion is crucial for powering AI models, enabling real-time analytics, and maintaining a comprehensive view of customer and operational data.
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                                                                                                    • Data Validation
                                                                                                      Description
                                                                                                      Data Validation is a data quality management technique that ensures the accuracy, consistency, and integrity of data within a system. In BFSI, this involves performing checks to ensure that input data conforms to predefined rules, formats, and business logic (e.g., ensuring a PAN number is in the correct format, or a loan amount falls within an acceptable range). Automated data validation, often powered by AI, minimizes errors at the point of entry or during processing, reducing the need for manual checks and enhancing data reliability for critical financial decisions.
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                                                                                                      • Data Standardization
                                                                                                        Description
                                                                                                        Data Standardization is the process of transforming data from disparate sources into a uniform format and consistent structure. In the BFSI sector, where data originates from numerous systems and in varied formats (e.g., different bank statement layouts, diverse customer data fields), standardization is critical. It enables banks and financial institutions to create a "single source of truth" for their data, facilitating accurate analytics, efficient cross-system integration, improved regulatory reporting, and robust AI model training.
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                                                                                                        • Data Reconciliation
                                                                                                          Description
                                                                                                          Data Reconciliation is the process of comparing two or more sets of data to identify and resolve discrepancies, ensuring accuracy and completeness across systems or records. In lending operations, this might involve comparing loan disbursement records with bank statements, or verifying customer payments against outstanding balances. Automated data reconciliation, often using AI, significantly enhances operational efficiency, strengthens regulatory compliance by reducing errors in financial reporting, and mitigates risks associated with inconsistent data.
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                                                                                                          • Loan Management System
                                                                                                            Description
                                                                                                            A Loan Management System (LMS) is a software platform designed to manage and automate various aspects of the loan lifecycle, from origination to servicing and collections. LMS centralizes loan data, automates workflows, handles calculations (interest, EMIs), generates statements, and tracks payment schedules. In conjunction with AI and IDP, modern LMS solutions enable lenders to streamline operations, reduce manual errors, improve efficiency, and enhance the overall customer experience by providing a comprehensive, digital framework for loan administration.
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                                                                                                            • Core Banking System
                                                                                                              Description
                                                                                                              A Core Banking System (CBS) is the central back-end system that processes daily banking transactions and manages customer data for a bank. It integrates various operations such as deposits, withdrawals, loan processing, account management, and customer information across all branches and digital channels in a centralized, real-time environment. Modern CBS are often integrated with AI solutions and IDP platforms (like Glib.ai's) via APIs to enhance fraud detection, automate processes, and provide real-time insights for improved operational efficiency and customer experience.
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                                                                                                              • Customer Relationship Management in BFSI
                                                                                                                Description
                                                                                                                Customer Relationship Management (CRM) in the BFSI sector refers to the strategies and technologies used by financial institutions to manage and analyze customer interactions and data throughout the customer lifecycle. A BFSI CRM system centralizes customer information, tracks communications, manages leads, and supports personalized service delivery. When integrated with AI, CRMs enable deeper customer segmentation, predictive analytics for personalized product offerings, automated service workflows, and enhanced client engagement, leading to improved customer satisfaction, loyalty, and business growth.
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                                                                                                                • Digital Lending
                                                                                                                  Description
                                                                                                                  Digital Lending refers to the process of providing loans entirely through online or mobile platforms, utilizing technology to automate various stages of the lending process from application to disbursement. This often involves AI-powered credit assessment, automated document verification, and seamless integration with digital payment systems. In India, digital lending is regulated by the RBI, which has introduced guidelines (e.g., RBI Digital Lending Directions, 2025) to ensure transparency, accountability, and customer protection in this rapidly growing sector.
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                                                                                                                  • Alternative Credit Data
                                                                                                                    Description
                                                                                                                    Alternative Credit Data refers to non-traditional data points used in credit assessment that go beyond standard credit bureau reports (e.g., CIBIL scores). This includes data from bank statements (cash flow, spending patterns), utility bill payments, GST returns for businesses, rental history, and digital footprints. Leveraging AI and Machine Learning, alternative credit data allows lenders to assess the creditworthiness of "thin-file" or "new to credit (NTC)" borrowers, expanding financial inclusion and enabling more accurate risk assessment, especially in emerging markets like India.
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                                                                                                                    • Early Warning Systems
                                                                                                                      Description
                                                                                                                      Early Warning Systems (EWS) in BFSI are proactive, AI-driven solutions designed to identify and flag potential signs of financial distress or risk in loan accounts or customer portfolios before they escalate into defaults or significant problems. EWS continuously monitor various indicators (e.g., irregular repayments, sudden income drops, unusual transaction behavior, declining credit scores) and generate automated alerts for financial institutions. Mandated by regulators like the RBI, EWS enable timely intervention, proactive risk mitigation, and better management of non-performing assets (NPAs).
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                                                                                                                      • Special Mention Accounts
                                                                                                                        Description
                                                                                                                        Special Mention Accounts (SMA) are a classification used by banks in India, as per RBI guidelines, to identify accounts that show early signs of incipient stress, potentially before they become Non-Performing Assets (NPAs). SMAs are categorized based on overdue periods (e.g., SMA-0: overdue 1-30 days; SMA-1: overdue 31-60 days; SMA-2: overdue 61-90 days). AI-powered loan monitoring and Early Warning Systems (EWS) are crucial tools for banks to accurately track and manage SMA accounts, enabling timely corrective actions to prevent further deterioration of asset quality.
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                                                                                                                        • Non-Performing Assets
                                                                                                                          Description
                                                                                                                          Non-Performing Assets (NPA), also known as non-performing loans, are a classification for loans or advances where the principal or interest payment has remained overdue for a specified period, typically 90 days or more. NPAs represent a significant risk to banks' profitability and stability. AI and predictive analytics are increasingly used in BFSI to identify high-risk accounts early (EWS, SMA), automate collections strategies, and improve underwriting to reduce the accumulation of NPAs in loan portfolios.
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                                                                                                                          • Credit Bureau
                                                                                                                            Description
                                                                                                                            A Credit Bureau (also known as a credit reporting agency) is an organization that collects and maintains credit information on individuals and businesses from various lenders and financial institutions. In India, major credit bureaus include CIBIL, CRIF High Mark, Experian, and Equifax. They use this data to generate credit reports and credit scores, which lenders use to assess a borrower's creditworthiness. AI-powered credit assessment and borrower profile enrichment solutions often integrate directly with credit bureaus to pull and analyze this data in real-time.
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                                                                                                                            • API
                                                                                                                              Description
                                                                                                                              An API (Application Programming Interface) is a set of defined rules and protocols that allows different software applications to communicate and interact with each other. In financial technology, APIs are crucial for building interconnected ecosystems. They enable seamless integration between a bank's core systems (e.g., CBS, LMS) and third-party fintech solutions (e.g., Glib.ai's IDP, fraud detection engines, credit bureaus), facilitating secure data exchange, automating workflows, and enabling the rapid deployment of new digital financial services.
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                                                                                                                              • MLOps
                                                                                                                                Description
                                                                                                                                MLOps (Machine Learning Operations) is a set of practices that aims to streamline the entire lifecycle of Machine Learning models, from development and training to deployment, monitoring, and maintenance in production environments. In the BFSI sector, MLOps is critical for ensuring that AI models used for credit scoring, fraud detection, and risk management remain accurate, reliable, scalable, and compliant over time. It bridges the gap between data science and IT operations, automating model validation, drift detection, and continuous integration/continuous deployment (CI/CD) for AI systems.
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