AI Finance Club’s Post

IFRS 9 and data science maturity in banks The full (and somewhat “ideal”) implementation of IFRS 9 in banks implies the use of dynamic models. In order to duly evaluate the monthly impairment of assets - represented by the changes in point-in-time ECL, models need to incorporate timely information. It means banks need to collect, treat and store information in a timely manner. Or - in other familiar words - do a timely ETL (Extract, Transform and Load) of information from credit bureaus, economic forecasters, market research platforms and internal systems. This requires a huge effort of the data and tech teams. Also, this is an opportunity of business development because: 1. Inputs from provision models can be used for a better credit lifecycle risk and capital management; 2. The evolution of data platforms, data streams and risk modeling maturity can lead to better models for other purposes, such as underwriting, collection, upsell, cross-sell and so on; 3. Despite the questionable reasons to advocate for standardization of risk in the financial system, there is a huge comparative advantage in terms of gaining investors trust. What else would you add to this list?

Poor data integrity is the fastest growing risk to financial services.   In the past few years with vendors and employee information changing rapidly, it became challenging to maintain accurate data in a secure and regulated way. These changes exposed weaknesses in existing manual processes and data management of financial organizations. Data integrity failings cost the typical organization between 15–25% of its revenue. Low risk starts with high-quality data. Nearly 30% of financial organizations say mistakes from manual processes are their biggest data reconciliation pain points. As technology advances, organizations are starting to automate tasks that were previously done manually to keep up with the trend towards digitalization. By using a software monitoring approach, organizations can quickly generate reports and reduce the need for manual work and its costs. Additionally, it allows individual users to easily identify and fix any issues with the data. Removal of manual labor is key in enabling them to work faster and more efficiently — and still survive the scrutiny of regulation. Nearly 70% of financial organizations expect new solutions that automate manual processes to be one of their top greatest investment areas in the next three years.    Now is the time to work proactively, utilizing monitoring tools and solutions as data increases in quantity and complexity, making automation essential for data integrity. For More information, see here: https://lnkd.in/gBhvUNJg 

💡 Revolutionize Your Data Management with Automation! 🌐 Data is the new currency, and managing it effectively is crucial. As such, efficiently capturing and processing “data” is key to staying ahead. With PowerCred's Intelligent Document Parser (IDP), you can automate data workflows, reduce errors, and boost productivity. Our latest blog explores the evolution of data capture and its impact on business efficiency. Perfect for finance leaders, risk managers, and credit officers looking to innovate. 🔍 Discover the future of data management automation! Read the Blog Now. https://lnkd.in/gPwU-jCg #businessefficiency #dataautomation #aiinfinance #powercred

How Manual Processes Cost Billions: The Case of JP Morgan's Excel Error The story of JP Morgan's $6 billion loss due to an Excel error in 2012 is a stark reminder of the dangers of relying on manual processes in complex tasks. Let's delve deeper and explore how digitalization and automation can prevent such mistakes. The Pitfalls of Manual Data Transfer: Human Error: In JP Morgan's case, a seemingly simple error of using "sum" instead of "average" during data transfer between spreadsheets had a catastrophic impact. This highlights how even the most skilled professionals can make mistakes, especially with repetitive tasks. Digitalization and Automation to the Rescue: Automated Data Transfer: Integrating financial models with databases and other systems can eliminate the need for manual data transfer altogether. This reduces human error and ensures data consistency. Data Validation Rules: Implementing automated data validation rules within the system can catch errors during data entry. For example, the system can ensure all numbers fall within a specific range or follow a particular format. Version Control and Audit Trails: Version control systems track changes made to models, allowing for easy identification and rollback of errors. Similarly, audit trails provide a record of data manipulation, making it easier to pinpoint the source of discrepancies. Beyond Excel: Embracing Advanced Tools: Specialized Financial Modeling Software: Dedicated financial modeling software offers robust features for complex calculations, risk analysis, and scenario planning. These tools often come with built-in safeguards and error-checking mechanisms. Machine Learning and AI: Advanced technologies like machine learning can automate repetitive tasks and identify anomalies in data. This can significantly improve the accuracy and efficiency of financial modeling. The Takeaway: The JP Morgan incident serves as a cautionary tale. Digitalization and automation offer powerful tools to minimize human error, ensure data integrity, and ultimately, protect businesses from costly mistakes. By leveraging these technologies, companies can build more robust and reliable financial models, fostering better decision-making and risk management. Contact us if you need support on transforming your manual processes to precise , automated and optimized ones

Data chaos in financial institutions? Say no more! 📉 Are you, like millions of other businesses struggling with these challenges? 📌 Varied Formats: Multiple templates and layouts pose challenges for traditional OCR solutions built for structured documents with fixed templates. 📌 Data Quality: Manual entry is prone to mistakes, impacting the quality of your insights. 📌 Complexity: Nested tables and relational databases require specialized skills for extraction. 📌 Volume: Extracting data from large volumes of documents is incredibly time-consuming. 📌 Compliance Risk: Manually ensuring consistent formats and documentation is fraught with human error. The answer to all your problems? Intelligent Document Processing 🧠 Intelligent Document Processing uses AI and machine learning to automate data extraction from the most complex financial statements. If you’re not using an IDP solution in your financial workflow, read our full article to discover how IDP can help achieve the true potential of your financial data - https://lnkd.in/gZKWA_xq #IDP #OCR #intelligent_document_processing #finance #banking

Yeas ago I had several arguments/opinions about how to streamline banking process, especially on credit report drafting, country risk report drafting, internal data management. For the first two, I always advocate that linking reliable data sources, creating more efficient data pipline, would elevate the efficiency significantly. Since most of the contents in a report presented by an analyst come from third party data source(client, provider, etc). Saving time for the analyst to focus other non-apparent matters will make a huge change. Now with LLM, RAG, ChainofThought, and many model design, workflow designs, the first thought came to me is to rethink the banking process design once again, since there is hugh potential to incorporate such tools to the organizational routines. However, if the foundation of data management is faulty, no organization can benefit the most from these new tools. Regarding the last one, internal data management. I still recall one quote from my colleague: An organization that relies heavily on manual Excel processing won't make too far. He was right, and is still right. Lots of automation with sound design would lower the risk of low quality data management. And today, I would argue that, if the organization(especially the ones which rely heavily on paperworks) has not incorprated agile methodology(and many more), or made decision to adapt organizational transformation to the new tech stack(LLM, ML, etc), the competitive disadvantage follows imminently. One more thing, either using LLM and related innovated workflow to handle immergent business senarios, or improving internal management and governance from bottom to top, make a quote, mark the source, be transparent, be responsible. #banking #LLM #datamanagement #reports

Revolutionizing IT Audits: Advanced Techniques for Enhanced Accuracy and Efficiency Tired of hearing how traditional IT audits are “good enough”? Sticking to outdated methods is a recipe for disaster. Advanced techniques like continuous auditing, data analytics, and machine learning algorithms are not just buzzwords—they're essential for staying ahead. Unlike periodic audits, continuous auditing provides real-time assurance by constantly monitoring and analyzing data. For instance, Bank of America adopted continuous auditing to monitor their transactions. This allowed them to detect anomalies instantly and reduce fraud risks by 35%, enhancing overall financial security. Sophisticated tools like Tableau and Power BI enable auditors to analyze large datasets, uncovering patterns and outliers that manual methods might miss. For example, Cleveland Clinic used data analytics to audit their patient billing systems. This approach identified billing errors and inefficiencies, ultimately saving the organization millions of dollars annually. Algorithms automate complex tasks and predict potential issues. Google implemented machine learning to audit its software development lifecycle. By identifying code vulnerabilities and compliance gaps early, they reduced security incidents by 40% and sped up their release cycles, ensuring more robust software. Follow me @Emmanuel Ofili for more insightful content as it relates to the GRC landscape.

💹 Empowering Finance with Data Analytics 💡 The finance industry is harnessing the power of data analytics to drive innovation, efficiency, and strategic decision-making. Here's how data analytics is transforming the finance sector: Risk Management and Mitigation 📉: Advanced analytics models assess and predict risks by analyzing vast amounts of data. This allows financial institutions to proactively manage risks, ensure regulatory compliance, and maintain financial stability. Fraud Detection and Prevention 🔍: Data analytics identifies suspicious patterns and anomalies in real-time transactions, enabling swift detection and prevention of fraudulent activities, thus safeguarding assets and enhancing security. Investment Strategies 📈: By analyzing historical data and market trends, financial analysts use data analytics to develop predictive models and algorithms that inform investment decisions and optimize portfolio management. Customer Insights and Personalization 💼: Financial institutions leverage customer data to offer personalized financial products and services. This enhances customer experience, satisfaction, and loyalty. Operational Efficiency ⚙️: Data analytics streamlines processes by identifying inefficiencies and optimizing workflows. This leads to reduced operational costs and improved service delivery. Credit Scoring and Loan Approvals 🏦: Traditional credit scoring methods are enhanced with alternative data and machine learning algorithms, resulting in more accurate credit assessments and broader access to credit. Regulatory Compliance 📝: Analytics tools help monitor compliance with financial regulations by automating reporting and ensuring adherence to regulatory requirements, reducing the risk of non-compliance. Market Analysis and Forecasting 🌐: Financial institutions use data analytics to analyze market conditions, economic indicators, and competitive landscapes, enabling better strategic planning and market positioning. Customer Retention and Acquisition 📊: Predictive analytics identifies at-risk customers and helps implement targeted retention strategies. Additionally, data-driven marketing campaigns attract new customers by understanding and addressing their needs. Financial Planning and Budgeting 🧮: Data analytics enables accurate financial forecasting and budgeting by analyzing past financial performance and predicting future trends, ensuring informed decision-making. Data analytics is a game-changer for the finance industry, providing the insights and tools needed to navigate complexities, mitigate risks, and seize opportunities. The potential for further innovation and growth in this field is immense, and I'm excited to see how it will continue to shape the future of finance. #Finance #DataAnalytics #RiskManagement #InvestmentStrategies #CustomerExperience #BigData #AI #RegTech #DigitalTransformation #Innovation

How to re-engineer a business process: 1. Identify gap during testing target state SF spot capital ERCF automated reporting (for which I also designed and developed SQL) in lower environment. Drill down the report data to isolate missing SF CRT deals as the source of the discrepancy. 2. Confirm these are new issue CRT deals and their capital relief effective month by reviewing private placement memorandum or term sheet. Trace the data flow upstream from database source for the SQL to the SF CRT model engine and confirm the engine is not vending spot capital data to the data warehouse for these new CRT deals. 3. Set up meeting with finance manager and SF CRT model system senior engineer to review findings. Confirm the model engine is not processing new CRT deals because of latency in inputs (including 3rd party source), also confirm no production gaps as the current process accepts modeled outputs for new CRT deals from upstream system. 4. Draft multi-month roll-over target-state integrated business/data/engine process flow identifying inputs/outputs, sources/destinations addressing input latency or gaps. Review internally with senior finance director and CRT model engine director. 5. Set up meetings with upstream stakeholders and lead/facilitate meetings to review proposed flow with senior directors/POCs: CRT business and modeling teams, CRT data team, data team upstream from model engine. Align stakeholders on deliverables, SLAs, and communication channels. 6. Design and execute system integrated testing with all stakeholders, systems, data sources, automated and manual processes. Validate automated SF spot capital ERCF report, share report and validation results with finance team for review and approval. 7. Assist stakeholders with updating procedures and internal controls as needed.