Cascading

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Cascading is a transformative AI tool designed to streamline and automate the loan origination process for banks and financial institutions.

How Cascading can help you:

Automates 90% of tasks, drastically reducing manual effort.
Tripling applicant conversion rates through efficient processing.
Deploys within 2 weeks for rapid integration and results.
Re-engages cold applicants automatically to keep leads warm.

Why choose Cascading: Key features

AI Loan Assistant for pre-qualification and instant follow-ups.
Online Application process streamlined for quick responses.
Efficient Document Collection through automated reminders.
Lead Recapture to re-engage applicants with no extra effort.
Customizable Intelligent Workflows to fit specific processes.

Who should choose Cascading:

Banks and credit unions looking to improve their loan application process.
Non-bank lenders seeking to automate and accelerate loan origination.
Financial institutions aiming for higher conversion rates and efficiency.

About Cascading

Website

https://www.cascading.ai

Release Date

March 2024

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Credit data powers Experian's software transformation - SiliconANGLE

In the course of nearly 30 years of collecting data about consumer spending patterns, Experian plc has amassed detailed information on more than 1 billion people and 25 million U.S. businesses. As one of the "Big Three" credit reporting bureaus, along with TransUnion LLC and Equifax Inc., it collects the information that's vital for financial institutions making decisions about whom to lend to and how much. When big-data analytics took the financial world by storm a few years ago, Experian realized it was sitting on a gold mine. In 2019, it began engineering a transformative expansion into software and data analysis, creating a business that now brings in 35% of the company's $7 billion in annual revenues. Software is now at the heart of Experian's growth strategy. "I joined 10 years ago, and the company now is almost unrecognizable," said Alex Lintner, chief executive officer for Experian Software and Technology. 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Its customers combine that data with external and internal information sources to build models that guide their decisions. All data is anonymized to comply with regulations. Experian has automated much of the grunt work of whipping data into shape to make it analytics-worthy. "We cleanse the data and to make sure it's complete, accurate, in the right formats and fresh," Lintner said. "Then we have an analytical sandbox environment where data scientists can run their Python, R or SAS code to build a model." Experian Assistant, a generative AI tool the company announced last October, aids in coding, enhances model transparency and helps data scientists cycle through multiple iterations quickly with advice for optimal coding and deployment. It recently won the 2025 FinTech Breakthrough Award for Analytics Innovation. "It doesn't replace a data scientist; it's like having an Experian consultant sitting next to you," said Keith Little, president of Experian Software Solutions. 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SiliconANGLE

Fri, 18 Apr, 4:11 PM UTC

Credibly Announces the Launch of Credibly.ai - an Innovation and Technology Showcase for Automation and Generative AI Use Cases in Fintech

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Benzinga

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From Invisible to Bankable: AI's Role in Bridging the Credit Gap

By Sudeep Garg, Chief Product Evangelist - Lending, Nucleus Software AI and the Future of Lending: A Smarter, More Inclusive Approach The financial services industry is undergoing a profound transformation, with artificial intelligence (AI) at its core. Lending, traditionally governed by rigid financial metrics and conventional credit histories, is evolving into a more dynamic and inclusive system. AI is not just an innovation in lending -- it is a necessity. It enables financial institutions to reach untapped markets, assess risk with greater accuracy, and drive financial inclusion at an unprecedented scale. For decades, access to credit was largely restricted to individuals with formal financial records. This left millions -- especially in developing economies -- outside the formal banking ecosystem. According to the World Bank, over 1.4 billion adults globally remain unbanked, unable to secure credit to grow businesses, support families, or invest in their futures. Without access to structured lending, many are forced into informal borrowing channels with high interest rates and exploitative terms. AI is changing this reality by leveraging alternative data sources -- ranging from utility bill payments and mobile usage patterns to e-commerce transactions and digital wallet behavior -- to construct financial profiles where traditional credit scoring falls short. Unlike static credit models, AI-driven systems continuously learn and adapt. By processing vast amounts of real-time data, these models identify behavioral patterns, predict defaults before they happen, and enhance fraud detection mechanisms. AI's ability to dynamically assess risk allows lenders to offer customized credit solutions tailored to an individual's financial capacity rather than relying on outdated credit assessment methodologies. This not only improves loan accessibility but also ensures that borrowers receive fair and structured financial products that align with their needs. Beyond Credit Scores: The New Age of AI-Driven Lending AI is revolutionizing lending far beyond credit assessment. Today, predictive analytics enable real-time monitoring of borrower behavior, allowing lenders to proactively manage risk and mitigate potential defaults before they occur. AI-powered loan servicing platforms can detect early signs of financial distress and trigger personalized repayment solutions, preventing unnecessary delinquencies. Fraud detection is also becoming more sophisticated, with AI continuously scanning transactions for anomalies and flagging suspicious activities before they escalate. Another significant breakthrough is AI's role in automating document verification. By intelligently assessing image quality and ensuring compliance with submission standards, AI reduces lead rejections and expedites the lending process. Additionally, the integration of voice bots and AI-driven customer service solutions is transforming the borrower experience, enhancing response times and improving overall accessibility to financial services. With the rise of Generative AI, lenders can now simulate multiple financial scenarios, forecast credit risk strategies, and design hyper-personalized loan products. This convergence of AI-driven insights and predictive modeling is redefining lending strategies, making them more responsive, efficient, and scalable. Fintechs, NBFCs, and the New Credit Revolution While traditional banks are steadily integrating AI into their lending frameworks, fintech firms and non-banking financial companies (NBFCs) are leading the charge. These agile players are harnessing AI-powered platforms to assess creditworthiness at scale, making financial services more accessible to previously unbanked populations. By merging alternative data with AI-driven risk assessment models, fintech lenders are reducing borrowing costs and fostering greater trust between institutions and consumers. Collaboration is a key driver of this transformation. AI-powered lending ecosystems are now leveraging partnerships with telecom providers, e-commerce platforms, and digital wallets to extract deeper behavioral insights. This integrated approach ensures a more holistic risk evaluation process, democratizing credit access while simultaneously enhancing fraud prevention measures. AI's ability to ensure transparency in credit assessments also strengthens borrower confidence, as customers receive clear, data-backed explanations for loan approvals or rejections. The Ethical Imperative: Responsible AI for Fair and Transparent Lending While AI holds immense potential to revolutionize lending, its widespread adoption comes with challenges. Algorithmic biases, lack of transparency, and regulatory concerns remain critical hurdles. AI models, if trained on biased historical data, can inadvertently reinforce existing inequalities. To counter this, financial institutions must invest in Explainable AI (XAI) frameworks that provide clear, auditable reasons for loan decisions. Ensuring that AI-driven assessments are transparent, fair, and accountable will be essential in fostering long-term trust in AI-powered financial services. Governments are also recognizing AI's potential to drive financial inclusion, leading to initiatives such as the "Grameen Credit Score." This initiative aims to provide underserved communities with formal credit access by analyzing alternative financial data, including UPI transactions, government subsidy receipts, and utility payments. Similarly, the Unified Lending Infrastructure (ULI) is creating a seamless, AI-integrated framework to connect multiple lenders, data sources, and credit assessment tools, further transforming digital lending accessibility. Regulatory oversight will play a crucial role in ensuring ethical AI adoption. Continuous model validation, adherence to evolving compliance frameworks, and data privacy measures must be prioritized to prevent misuse and maintain borrower protection. AI is not just reshaping the financial landscape; it is redefining the very principles of responsible lending. Conclusion: The Future of Credit is AI-Powered, Intelligent, and Inclusive AI-driven lending is no longer a futuristic concept -- it is today's reality. Financial institutions that embrace AI strategically will not only drive operational efficiency but also contribute to a more inclusive, equitable financial ecosystem. From predictive analytics and fraud prevention to AI-powered loan servicing and tailored credit solutions, the opportunities for AI in lending are vast. However, with great power comes great responsibility. Ensuring that AI-driven credit decisions are ethical, unbiased, and transparent will be paramount in building a sustainable, AI-powered financial future. As the industry moves forward, collaboration between banks, fintech innovators, NBFCs, and regulatory bodies will be the key to unlocking the full potential of AI in lending. Those who adopt AI with a responsible, customer-centric approach will not only stay ahead of the curve but will also shape the future of finance itself. (The author is Sudeep Garg, Chief Product Evangelist - Lending, Nucleus Software, and the views expressed in this article are his own)

CXOToday.com

Mon, 19 May, 12:51 PM UTC

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