Accounting Software Features

Excited to share my latest dive into the intersection of high-speed data and financial regulation! As digital assets and tokenized securities gain momentum, the critical question is: How do we maintain an unquestionable, tamper-proof audit trail at massive scale? Traditional databases often fall short. My new article explores how Apache Kafka's core architecture, the immutable commit log, serves as the ideal compliance layer for regulated asset transfers. I cover: 1. The power of immutability for audit-readiness. 2. Using Schema Registry to enforce structured compliance events. 3. Enabling real-time AML/KYC checks using stream processing. 4. Strategies for long-term, WORM (Write Once, Read Many) archival. If you are building infrastructure for Fintech, Digital Assets, Trading Systems, or are focused on #RegTech, you need to see how Kafka can move compliance from an "afterthought" to a real-time capability. https://lnkd.in/g_G3myVH #Kafka #DigitalAssets #Fintech #Compliance #RegTech #StreamingData #Auditability

Are you buried in reports? "Can't see the wood for the trees." Every Salesforce admin has been there: → Endless lists of reports → No idea what's still relevant → Wasted time searching for that one critical report The solution? Create a "Report on Reports". Here's how: Set up a custom report type (Setup > Report Types > New) → Primary Object - Reports → Secondary Object - Dashboard Components Save and run a new report. ↳ Show all reports ↳ Include valuable columns like 'Report Name', 'Folder Name', 'Created Date' and 'Last Run' Now you can: → Filter by "Last Run" to identify reports that haven't been touched in years → Group by Report Type to see which custom types are not being used → Add Dashboard Component field to see if it's used by a dashboard What other report-ception tricks do you know? --- Found this helpful? Like 👍 | Comment ✍ | Repost ♻️

Following numerous inquiries about API architectural patterns, I'm sharing this comprehensive technical analysis I developed to help teams make informed decisions about their API strategy. 𝗥𝗘𝗦𝗧 (𝗥𝗲𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗦𝘁𝗮𝘁𝗲 𝗧𝗿𝗮𝗻𝘀𝗳𝗲𝗿) • Industry-standard architecture leveraging HTTP methods • Excellent for CRUD operations and stateless interactions • Benefits from broad tooling support and cached responses • Best suited for resource-oriented services 𝗚𝗿𝗮𝗽𝗵𝗤𝗟 • Query language enabling precise data fetching • Eliminates over-fetching/under-fetching of data • Strongly typed schema serving as a contract between client and server • Particularly valuable for complex data relationships and microservice aggregation 𝗪𝗲𝗯𝗦𝗼𝗰𝗸𝗲𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹 • Enables full-duplex, persistent connections • Significantly reduces overhead for real-time applications • Ideal for live dashboards, gaming, and collaborative tools • Maintains a single TCP connection for bi-directional data flow 𝗴𝗥𝗣𝗖 (𝗚𝗼𝗼𝗴𝗹𝗲 𝗥𝗲𝗺𝗼𝘁𝗲 𝗣𝗿𝗼𝗰𝗲𝗱𝘂𝗿𝗲 𝗖𝗮𝗹𝗹) • Leverages HTTP/2 for multiplexed streaming • Protocol Buffers enable efficient serialization • Generates type-safe client libraries automatically • Optimal for microservice-to-microservice communication 𝗠𝗤𝗧𝗧 (𝗠𝗲𝘀𝘀𝗮𝗴𝗲 𝗤𝘂𝗲𝘂𝗶𝗻𝗴 𝗧𝗲𝗹𝗲𝗺𝗲𝘁𝗿𝘆 𝗧𝗿𝗮𝗻𝘀𝗽𝗼𝗿𝘁) • Lightweight publish/subscribe messaging protocol • Designed for high-latency or unreliable networks • Minimal bandwidth consumption • Essential for IoT and sensor networks 𝗪𝗲𝗯𝗵𝗼𝗼𝗸 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 • Event-driven HTTP callbacks • Enables asynchronous system integration • Reduces polling overhead • Perfect for event notifications and integrations Technical Decision Framework: 1. Performance Requirements: Consider gRPC for internal services requiring maximum throughput 2. Real-time Needs: WebSocket for bi-directional communication 3. Resource Constraints: MQTT for devices with limited bandwidth 4. API Consumption Patterns: GraphQL for varying client requirements 5. Development Velocity: REST for rapid implementation and broad compatibility I would appreciate hearing about your experiences implementing these patterns in production environments.

📘 The Ultimate Guide to AI for Accountants (Especially for Indian CAs) 🇮🇳🤖 If you're an accountant or CA and wondering whether AI is just a buzzword or a real game-changer — this post is for you. Here’s a no-fluff guide on how Artificial Intelligence is changing the accounting profession, with tools, use cases, and actionable ideas for Indian finance professionals. 🧠 First Things First: What Exactly Can AI Do in Accounting? AI is not here to replace accountants. It’s here to: Automate time-consuming tasks ✅ Reduce manual errors ✅ Help us focus on higher-value advisory work ✅ 🔍 Where AI Is Already Making an Impact 1. Audit & Assurance AI Use Case: Detects anomalies in thousands of ledger entries using pattern recognition. Tools: MindBridge AI , My Audit Result: Smarter, faster audits with full-population testing—not just samples. 2. GST & Tax Compliance AI Use Case: Matches GSTR 2B with purchase records and auto-prepares returns. Tools: ClearTax GST, RazorGST Result: Hours saved on reconciliation and fewer errors/notices. 3. TDS & ITR Filing AI Use Case: Auto-calculates late fees, prepares Form 16s, flags mismatches. Tools: Webtel.,Winman, Saral TDS Result: Speeds up bulk filings and ensures compliance with the latest CBDT changes. 4. Invoice Processing & Data Entry AI Use Case: Extracts data from scanned invoices and plugs it into accounting software. Tools: Zoho Books (AI OCR), Tally automation plugins. Result: No manual entry, fewer errors, real-time data availability. 🛠️ Real Tools Indian CAs Are Using ClearTax GST – for reconciliation, invoicing, e-invoice readiness. Winman + Saral – for ITR + TDS automation. Zoho Books / Tally for invoice data extraction and syncing. 👨💼 So, How Does This Help YOU? Whether you’re a solo practitioner or managing 100 clients, AI gives you: More time to focus on strategic work 📈 Fewer errors = happier clients ✅ Scalability during peak seasons (like March, July, September) 📊 Competitive edge in a tech-driven market 💼 🧭 Where Should You Start? 🔹 Pick one pain point (GST? TDS? Audit?) 🔹 Choose one AI-enabled tool 🔹 Train your team (or yourself) on it 🔹 Track ROI – see how much time or money it saves over 1 quarter It’s that simple. 🙋♂️ Final Thoughts: The future of accounting is not about man vs. machine. It’s about man WITH machine. If you’re a CA or accountant, AI won’t replace you. But someone using AI probably will. Drop your questions or share what’s worked for you. Let’s build a #DigitalCA community that thrives on collaboration and growth! #AIforAccountants #IndianCAs #FutureOfFinance #DigitalCA #CACommunity #AccountingTools #FinTechIndia #SmartCompliance #ClearTax #AuditAutomation #GSTTech #ICAI #CharteredAccountants #TechInPractice #CA #India

Excel spreadsheets can be transformed into secure apps, letting analysts extend formulas into personalized, user-driven reporting. Dynamic reporting often means building multiple pivot tables or filters. With Copilot, analysts can produce natural language summaries directly. In Excel, you might use =COPILOT("Summarize sales by region", Sales!A2:C200) to generate a text summary. Run another formula like =COPILOT("Highlight top 5 products", Sales!B2:B200) for rankings. Combine outputs into a dashboard sheet. This creates value, but requires analysts to manage every formula variation. In Sheetcast, reporting prompts can be user-driven. Builders define a text input field where visitors enter a reporting question. That field links to the formula: =COPILOT(UserPrompt, Sales!A2:C200). The result displays in a report page, automatically aligned to the dataset. Permissions ensure visitors only see fields relevant to them, even while running their own prompts. Instead of maintaining many static views, the app enables dynamic reports generated by Copilot on demand. Analysts keep control of data security, while users explore insights with natural language. #sheetcastpartner

Financial reporting should be about strategic decision-making, not manual data wrangling. Yet, finance teams still spend days pulling data, reconciling numbers, and formatting reports—only to find errors at the last minute. The process is time-consuming, prone to mistakes, and slows down critical business decisions. Robotic Process Automation (RPA) with tools like UI Path is transforming financial reporting. Instead of manually extracting, cleaning, and consolidating data, automation does it for you—accurately, in real time, and without delays. Here’s how it works: ✅ Data is automatically pulled from multiple sources (ERP, CRM, spreadsheets, banks). ✅ Reconciliations happen instantly, reducing errors and improving accuracy. ✅ Reports are generated in minutes—standardized, formatted, and audit-ready. Without automation, finance teams are stuck in reactive mode, spending 80% of their time on report preparation and only 20% on analysis. The result? Slower decision-making, frustrated CFOs, and outdated insights. A company that automated its reporting process cut preparation time by 60%—freeing up finance teams to focus on forecasting, strategy, and real business impact. If your team is still manually preparing reports, you’re already behind. It’s time to automate and turn your finance team into a real-time data powerhouse. 📩 Let’s talk about how RPA can transform your financial reporting. Drop a comment or send me a message if you’re ready to make the shift! #Automation #RPA #FinanceTransformation #CFO #FinancialReporting

APIs aren't just endpoints for data engineers - they're the lifelines of your entire data ecosystem. Choosing the Right API Architecture Can Make or Break Your Data Pipeline. As data engineers, we often obsess over storage formats, orchestration tools, and query performance—but overlook one critical piece: API architecture. APIs are the arteries of modern data systems. From real-time streaming to batch processing - every data flow depends on how well your APIs handle the load, latency, and reliability demands. 🔧 Here are 6 API styles and where they shine in data engineering: 𝗦𝗢𝗔𝗣 – Rigid but reliable. Still used in legacy financial and healthcare systems where strict contracts matter. 𝗥𝗘𝗦𝗧 – Clean and resource-oriented. Great for exposing data services and integrating with modern web apps. 𝗚𝗿𝗮𝗽𝗵𝗤𝗟 – Precise data fetching. Ideal for analytics dashboards or mobile apps where over-fetching is costly. 𝗴𝗥𝗣𝗖 – Blazing fast and compact. Perfect for internal microservices and real-time data processing. 𝗪𝗲𝗯𝗦𝗼𝗰𝗸𝗲𝘁 – Bi-directional. A must for streaming data, live metrics, or collaborative tools. 𝗪𝗲𝗯𝗵𝗼𝗼𝗸 – Event-driven. Lightweight and powerful for triggering ETL jobs or syncing systems asynchronously. 💡 The right API architecture = faster pipelines, lower latency, and happier downstream consumers. As a data engineer, your API decisions don’t just affect developers—they shape the entire data ecosystem. 🎯 Real Data Engineering Scenarios to explore: Scenario 1: 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗙𝗿𝗮𝘂𝗱 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 Challenge: Process 100K+ transactions/second with <10ms latency Solution: gRPC for model serving + WebSocket for alerts Impact: 95% faster than REST-based approach Scenario 2: 𝗠𝘂𝗹𝘁𝗶-𝘁𝗲𝗻𝗮𝗻𝘁 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 Challenge: Different customers need different data subsets Solution: GraphQL with smart caching and query optimization Impact: 70% reduction in database load, 3x faster dashboard loads Scenario 3: 𝗟𝗲𝗴𝗮𝗰𝘆 𝗘𝗥𝗣 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 Challenge: Extract financial data from 20-year-old SAP system Solution: SOAP with robust error handling and transaction management Impact: 99.9% data consistency vs. 85% with custom REST wrapper Image Credits: Hasnain Ahmed Shaikh Which API style powers your pipelines today? #data #engineering #bigdata #API #datamining

Spent the morning going deep into OpenAccountants. And honestly It is doing something I have not seen in AI tax tools before. Most tools today feel like wrappers Better UI Better prompts Same underlying guesswork This is different 👇 Two things stand out immediately: First Built by accountants Not a tech layer on top of tax But people who actually sign returns That changes everything What gets prioritised How edge cases are handled How risk is treated Second Open source While most tools stay closed and expensive This is MIT licensed 261 skills 172 jurisdictions Free to use Free to inspect Free to improve That alone is a big shift But what really makes it interesting is the architecture: It does not jump to conclusions It checks the supplier Then applies the rule Then defaults conservatively if unsure Every output is explainable Every number traces back to actual law And instead of pretending to automate everything It is built for review Which is how real accounting actually works No black box No blind filing Just structured decisions with human checkpoints If you are building or exploring in: Tax Fintech AI for compliance This is worth studying This is live on Product Hunt please would love to hear from you and get feedback https://lnkd.in/gJsXFBD8 My POV: The future of AI in accounting is not replacing accountants It is building systems that think like them

The 6th EU Anti-Money Laundering Directive (AMLD6), adopted in 2024, introduces significant updates to the EU’s framework for combating money laundering and terrorist financing. Here’s what you need to know: 📌 Key Highlights: • Beneficial Ownership Transparency: AMLD6 standardizes the definition and identification of beneficial owners, focusing on both ownership (25% threshold, reduced to 15% in high-risk sectors) and control. • Centralized Registers: Member states must maintain interconnected beneficial ownership registers accessible to competent authorities and entities with a legitimate interest. • Access Rules: While public access to some registers was restricted following a 2022 court ruling, AMLD6 ensures structured access for compliance professionals, journalists, and NGOs. • Enhanced Data Verification: Authorities are required to validate the accuracy of beneficial ownership data, ensuring historical records are maintained for up to 15 years. 📌 Broader Scope: • Expanded Obliged Entities: New sectors, including crypto-asset providers, art traders, and professional football clubs, are now subject to compliance. • Customer Due Diligence (CDD): Obliged entities must identify and verify both clients and their beneficial owners, monitor relationships, and assess risks. These measures aim to bolster transparency, streamline compliance across the EU, and prevent illicit financial activities. Organizations operating in affected sectors should prioritize aligning their compliance frameworks with the directive’s requirements. #AMLD6 #Compliance #LegalUpdates #AML

Canva for Football Scout Reports 🎨⚽ One of the biggest benefits of building my own platform is having a playground to experiment, learn, and push boundaries without constraints. I'm proud of the player profiles I've developed - but I kept hearing the same feedback: "Can I customise this for my own reports?" So I built it. The Scout Report Builder pulls player data directly into a drag-and-drop canvas where you can:  → Add radar charts, scatter plots, stats tables, recent form  → Full customisation on every element - colours, sizes, positioning  → Include your own branding and commentary  → Export high-quality images ready for presentations The idea is simple: data-driven visuals to support your analysis, with your own perspective and insights layered on top. No code. No design skills. Just click, customise, drag and drop. This was an ambitious feature to land with a huge ceiling for where it can go - so I'd genuinely appreciate any feedback from scouts, analysts, or anyone who creates player reports. What would make this useful for your workflow?