Financial Technology Solutions

🛠️ 𝗢𝗽𝗲𝗻-𝗔𝗰𝗰𝗲𝘀𝘀 𝗧𝗼𝗼𝗹𝘀 𝗳𝗼𝗿 𝗰𝗹𝗶𝗺𝗮𝘁𝗲 𝗮𝗻𝗱 𝗱𝗶𝘀𝗮𝘀𝘁𝗲𝗿 𝗿𝗶𝘀𝗸 𝗺𝗼𝗱𝗲𝗹𝗹𝗶𝗻𝗴 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗶𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝘆 The Insurance Development Forum’s Risk Modelling Steering Group put together a hub of free, open-access risk modelling tools covering exposure data, catastrophe model catalogues, parametric insurance design, and risk pooling.  A few worth highlighting:  🌍 Oasis Risk Explorer, a step-by-step guide to parametric insurance solutions  📋 CatRiskTools, a full catalogue of country-peril catastrophe risk models  🌊 Risk Pooling Tool, explores pooling risk effects on multi-peril or multi-region losses  💡 Parametric Insurance Case Studies, demonstrating applied structures and modelling They’ve also included links to the The World Bank Group’s Disaster Risk Financing (DRF) analytics tools and United Nations Office for Disaster Risk Reduction (UNDRR)’s Risk Information Exchange, an immense collection of data entries, sources and selected metadata useful for national and sub-national risk information systems. This is a fantastic collection of tools for anyone looking to spot and act on risk early!  Access the tools here: https://lnkd.in/eaGviKyb Insurance Development Forum #riskmanagement #insurance #climaterisk #parametricinsurance  

Microsoft D365 is quietly growing in demand across the ERP space. For years, SAP dominated the ERP-tax conversation. And while S/4HANA migration projects are still driving demand, there’s a parallel trend that’s accelerating: - Mid-market and multinational companies are moving to D365, and they need tax professionals who understand how VAT, indirect tax, and compliance tools integrate with it. Why does it matter? - D365 is increasingly the platform of choice for fast-growing businesses leaving legacy systems behind. - Companies are struggling to find tax professionals who can bridge D365 with tools like Vertex, Avalara, or OneSource. - Salaries are already creeping up, candidates with this niche are commanding salary premiums. D365 isn’t just “an IT thing.” It’s shaping the future of tax roles. If you’re in indirect tax and enjoy systems work, getting fluent in D365 could be one of the smartest career moves you make before 2026. Are you seeing D365 come up more in tax tech projects?

Understanding the Nuances: SAP FICA vs. SAP FICO SAP FICO: Overview: SAP FICO is a core component of SAP's ERP system, encompassing both Financial Accounting (FI) and Controlling (CO). Financial Accounting (FI): Focuses on external reporting, managing the general ledger, accounts receivable, accounts payable, bank accounting, asset accounting, and more. It's crucial for creating financial statements like balance sheets and profit & loss accounts. Controlling (CO): Deals with internal cost accounting, profit center accounting, and internal orders. It's all about planning, monitoring, and optimizing costs to enhance profitability. Integration: FICO integrates with various other SAP modules like Sales and Distribution (SD), Materials Management (MM), etc., for a holistic financial management approach. SAP FICA: Overview: SAP FICA is specifically designed as a sub-ledger for industries dealing with high-volume, customer-centric billing scenarios such as utilities, telecom, and insurance. Functionality: It extends beyond standard accounts receivable (AR) functionalities. FICA supports complex billing scenarios, mass processing of documents, and customer-specific billing, all while dealing with millions of transactions efficiently. Unique Features: High Volume Transaction Management: Capable of handling millions of transactions with optimized data storage and performance. Contract-Based Accounting: Manages receivables and payables based on customer contracts, which is not a standard feature in traditional FI-AR. Industry-Specific: Primarily used in sectors with many small transactions per customer, like utilities or telecom, where traditional AR systems might not suffice. Integration: While FICA integrates with FICO for financial reporting, it also works closely with billing components like SAP Billing and Revenue Innovation Management (BRIM) for convergent billing needs. Key Differences: Volume and Complexity: FICA is optimized for environments where you have thousands or millions of small transactions, whereas FICO handles broader financial reporting and accounting across the organization. Purpose: FICO is a general finance module for all businesses, while FICA is specialized for industries with specific billing and receivable management needs. Data Handling: FICA uses a different approach for data storage and retrieval due to its high-volume transaction capabilities, ensuring performance doesn't degrade with scale. Billing and Revenue: FICA integrates directly with billing solutions to provide end-to-end management from billing to collection, something that FICO does not handle with the same granularity. If you're in an industry dealing with large customer bases and complex billing, FICA might be your go-to solution. For comprehensive financial management across all sectors, FICO remains pivotal. #SAP #FICA #FICO #ERP #DigitalTransformation #Finance #Billing #RevenueManagement

Is this the end of InsurTech as we know it? Every six months, we compile data on the European InsurTech ecosystem using market research, deal tracking, and our proprietary scouting tools. Below is a summary of our recent webinar, highlighting key KPIs, major trends, and future expectations. 1/ InsurTech KPIs Investment headlines often focus on declines, but the picture is more nuanced in InsurTech. While the number of deals dropped to 61 last year, total funding reached €820m, a year-on-year increase. France led in total funds raised (thanks to massive rounds by Alan and Akur8), the UK topped deal activity, and Germany lagged behind. Outside these core markets, Switzerland and Spain showed strong performance, particularly in "emerging risks." To me, InsurTech investment trends mirror global VC and FinTech dynamics, showing a post-peak stabilization rather than sector-specific decline. 2/ Major Trends The post-pandemic shift from “growth at all costs” to “profitable growth” is reshaping the ecosystem. Startups like Mila and Acheel (France), Clark (Germany), Cuuva (UK), and EIR (Sweden) have achieved profitability, with others aiming to follow by optimizing CAC/LTV ratios and operational efficiency. Meanwhile, private rounds and cost-cutting measures dominated last year, but consolidation is increasing. Allianz Direct was notably active, acquiring Luko, iptiQ, and Friday. CEO resignations were frequent in 2024, with 10 companies—including unicorns like Wefox and Clark—publicly announcing leadership changes. This reflects challenges & opportunities in navigating profitability and market shifts. 3/ What’s Next? a/ AI in Insurance With 30% of InsurTech funding going to AI-first companies, and 18% of deals focused on AI, automation is set to transform the sector. Agentic AI, predicted as the next wave of RPA, could unlock operational efficiencies across the industry. b/ Embedded Insurance Long discussed, embedded insurance is finally gaining traction. Platforms like Qonto and Ornikar did integrate insurance into their ecosystems, reflecting a broader trend where platforms adopt financial services—and insurance is the natural next step. c/ Emerging Risks Startups addressing risks like cybersecurity, carbon credit insurance, and climate-related threats are on the rise, accounting for 20% of deals last year. This segment presents opportunities for technology and data-driven solutions to support incumbents in managing new risks. #insurance #insurtech #venturecapital

AI in insurance is not a productivity hack 🚫 Automating the past is safe and will generate marginal returns. The real value lies in underwriting the future! AI is being talked about everywhere in insurance. Too often, the conversation stalls at efficiency theatre. Faster underwriting. Cheaper claims handling. Fewer people doing more work. Useful, but small. The real opportunity sits elsewhere. Reimagining Risk in an AI-Driven World, developed by the International Insurance Society, captures this shift well. Having contributed to the report and led the executive workshop in Zurich, one message came through very clearly: the next decade will separate insurers making marginal improvements from those rebuilding their operating models around new forms of risk, data, and human judgement. AI is not the strategy. It is the unlock 🔓 The strategic upside is not incremental. It sits in: • New insurable risks emerging from intangible assets, cyber, AI, and climate • Proprietary knowledge graphs, data, decision systems become a true edge • Human judgement being augmented, not replaced, in a trust-based industry • Governance, talent, and data strategy becoming board-level differentiators, not IT issues 🤩 One stat should give leaders pause. Nearly 90% of firms are experimenting with GenAI, yet only around a quarter have anything in real production. Plenty of motion. Limited transformation. That gap is not about technology. It is about operating model courage. Keen to hear from peers across insurers, reinsurers, brokers, MGAs, and insurtechs: • Where have you seen AI move the needle beyond efficiency? • What is genuinely blocking scaled deployment? • Are we underwriting new risks fast enough, or just automating old ones? If insurance gets this right, we don’t just adapt to an AI-enabled world. We become one of its core stabilisers. Thoughts and counter-views welcome. Full report link in comments 👇 Anders Malmström, Joshua Landau, Colleen McKenna Tucker

Most insurance companies don’t have a product problem. They have a 𝐬𝐢𝐠𝐧𝐚𝐥 𝐩𝐫𝐨𝐛𝐥𝐞𝐦. Trouble shows up early for customers… and late for leadership. McKinsey’s 2025 analysis shows that only a small fraction of insurers capture meaningful value from AI and the reason isn’t model quality. It’s because 𝐝𝐚𝐭𝐚 𝐬𝐢𝐭𝐬 𝐢𝐧 𝐬𝐢𝐥𝐨𝐬 across underwriting, claims, support, and policy servicing. Another study highlights that predictive analytics when actually integrated can reduce loss ratios, speed up claims, and improve risk accuracy. But most insurers never reach that stage because their systems can’t surface early patterns. So what happens? A spike in confusion calls. Customers misusing features. Renewal expectations not matching policy reality. Claim friction rising quietly for weeks. By the time these signals hit dashboards, the damage is already in motion: lower NPS, rising churn, operational load, regulatory exposure. This is why insurance needs an 𝐈𝐂𝐔 - 𝐈𝐧𝐬𝐢𝐠𝐡𝐭 𝐂𝐨𝐫𝐫𝐞𝐜𝐭𝐢𝐨𝐧 𝐔𝐧𝐢𝐭. A team that: 1. Connects disparate data into a single, queryable layer. 2. Builds early-warning models for churn, fraud, sentiment, and claims delay. 3. Flags mismatches between expectation and experience in real time. 4. Routes insights directly into underwriting, ops, and customer teams. When insights arrive early, transformation doesn’t arrive late. And in insurance, 𝐭𝐡𝐞 𝐞𝐚𝐫𝐥𝐢𝐞𝐬𝐭 𝐬𝐢𝐠𝐧𝐚𝐥 𝐢𝐬 𝐭𝐡𝐞 𝐮𝐥𝐭𝐢𝐦𝐚𝐭𝐞 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐭𝐨 𝐰𝐢𝐧. #InsuranceIndustry #DataAnalytics #CustomerExperience #PredictiveAnalytics

After reviewing more pitch decks these past few days, I see African fintech founders are still flogging the dead horse that is "banking the unbanked" as a lazy fundraising pitch. From Yaounde to Cape Town, it’s the same story, another mobile wallet, payments app, another promise to bring financial inclusion to the masses. Truth is: most Africans are not unbanked because they lack access; they’re unbanked because they lack income. A new app won’t change that. The Brutal Truth Lack of Disposable Income – People don’t need more fintech solutions; they need more money. Without increased economic productivity, most “financial inclusion” solutions remain useless. Broken Unit Economics – Many fintechs rely on unsustainable VC fueled growth, acquiring “users” who don’t generate revenue. Regulatory Capture & Infrastructure Gaps – Governments protect banks and telcos dominate mobile money. The real bottlenecks are systemic, not just about "access." Startups often underestimate how slow, expensive, and political it is to scale across markets. Real Problems & Better Solutions Income-Generating Fintech – Instead of just moving money, fintech should help people make money. Platforms enabling gig work, SME financing, and export-focused businesses can drive real financial inclusion. A fintech that helps informal traders access larger markets, rather than just helping them "save." Decentralized Credit & Alternative Lending – Traditional credit models don’t work in Africa. Instead: Use supply chain data, mobile behavior, and transaction flows to build more dynamic credit models. Integrate fintech into cooperative lending structures like tontines or village savings groups, where trust already exists. B2B Payments & Trade Infrastructure – Cross-border trade needs work, killing SME growth. Fix it: Build better escrow and invoice financing tools that help African businesses transact across borders securely. Verticalized Fintech in High-Impact Sectors – Fintech should power real economic activity, not just payments. Agritech fintech: Give farmers access to dynamic pricing, supply chain finance, and better insurance. Healthcare fintech: Enable embedded payments and credit for medical services, helping people afford care without predatory loans. Logistics fintech: Provide financing for truckers, warehousing solutions, and real-time supply chain support. Infrastructure-First Fintech – If power, internet, & ID verification are problems, solve those first. Payments without stable connectivity? Build USSD-based financial services. Weak credit infrastructure? Build platforms that help lenders pool risk and share credit data across borders. The era of cheap fundraising gimmicks is over. African fintech must shift from vanity metrics to real impact, solving income generation, trade inefficiencies, and credit access at scale. I'm tired of saying this, founders who build with these in mind won’t need to beg for funding; investors will come looking for them.

The Insurance Industry Is at an Inflection Point – and AI Is Leading the Charge From outdated systems and unstructured data to rising customer expectations and talent shortages — insurers are under immense pressure. But with Generative AI, there’s finally a real way out. What’s Changing? 1. 60% of operational costs are still manual – AI can slash that. 2. 80% of data is untapped – GenAI reads, learns, and leverages it. 3. Only 18% of insurers currently use AI – but that’s about to change. Key Impact Areas: ✅ Underwriting: 90% data accuracy + new product models. ✅ Claims: 70% of simple claims can be auto-resolved + up to 50% faster processing ✅ Customer Experience: 48% higher NPS, 85% faster resolutions ✅ Fraud Detection: AI flags 75% of fraudulent claims in real time ✅ Sales & Distribution: AI agents, personalized funnels, smarter upsells ✅ Policy Admin: Real-time compliance, automated changes, predictive lapse alerts ✅ New Products: From behavior-based insurance to once “uninsurable” tech like drones & autonomy It’s not just about automating workflows. It’s about rethinking the very DNA of insurance using AI-first foundations. And those who don’t adapt — risk becoming obsolete. Whether you're transforming an incumbent or building the next vertical AI unicorn — the time is now.

Fixing Agriculture’s Core Issue: Market Linkage and Policy Bias!! Farmers feed the world, yet many struggle to access markets that fairly value their produce. This market linkage gap, combined with policies prioritizing cheap food for consumers, traps farmers in poverty, threatens food security, and stifles agricultural progress. With smallholders producing 70% of global food, solving this is urgent. Why It Matters Poor market access costs farmers billions—40% of produce in sub-Saharan Africa alone rots before reaching buyers. Meanwhile, policies like price caps and subsidies keep basic commodities like grains and rice affordable for consumers but depress farmgate prices, penalizing farmers. This dual challenge demands bold solutions. Key Barriers Weak Infrastructure: Poor roads and storage cause massive post-harvest losses. Information Gaps: Farmers lack real-time market data, leaving them vulnerable to exploitative value chains. Limited Networks: Smallholders miss out on large markets due to scale and connections. Financial Constraints: No credit means no investment in quality or technology. Policy Bias: Price controls and consumer-focused subsidies undervalue farmers’ work, as seen in systems like India’s MSP, which often favor select crops. Solutions That Work Tech Platforms: Apps today connect farmers to buyers, boosting incomes by 30%. Better Infrastructure: Public-private investments in roads and cold chains cut losses. Cooperatives: Models like Kenya’s Tea Agency show collective bargaining unlocks global markets. Value Addition: Training in processing or certifications opens premium markets. Fair Policies: Shift from price controls to income support and market diversification to balance consumer needs with farmer livelihoods. The Way Forward Low consumer prices shouldn’t come at farmers’ expense. Bridging market gaps and reforming biased policies can slash waste, boost incomes, and ensure resilient food systems. The impact—thriving farmers, stronger economies, and sustainable agriculture—is worth fighting for. Join the Conversation What’s working in your region to improve market access or fix policy imbalances? Share your ideas below—let’s build a fairer future for agriculture.

“52% of Gen Z and Millennials feel more comfortable sharing financial information with AI than explaining their finances to a human.” It comes from a recent report - https://lnkd.in/daaQhsdN — a large-scale study conducted in the US on how AI is reshaping personal finance. But honestly, when I look at #Bharat — especially Tier 1 and Tier 2 cities — I believe this shift may happen even faster here. Why? Because India has already normalized digital financial behavior at the population scale. #UPI changed everything. Today, millions of young professionals seamlessly use platforms like #CRED, #PhonePe, Google Pay, #Paytm, #Groww, Zerodha, and others not just for payments, but for credit, investing, expense tracking, rewards, lending, insurance, and wealth creation. The next layer on top of this ecosystem is not just “fintech.” It is #intelligent #finance. And this report highlights some very important behavioral shifts: • Over 55% of consumers already use AI for financial tasks • 86% say AI helps them understand finances better • Many users now trust AI more than traditional advisors for basic financial guidance • Consumers increasingly want AI to automate budgeting, savings and even investments • But simultaneously, they also want #transparency, #explainability and human oversight -> That last point is extremely important. As AI agents become capable of taking financial decisions on our behalf — recommending investments, optimizing taxes, reallocating savings, predicting spending behavior — financial literacy becomes more important, not less. Because tomorrow, your AI assistant may tell you: “Move ₹25,000 from here to there.” “Reduce exposure to this asset class.” “Pause this SIP.” “Take this insurance product.” “Refinance this loan.” And at that moment, you still need enough financial understanding to ask: “WHY?” That ability to #audit #AI #decisions may become one of the most critical life skills of the next decade. The future may not belong only to people who understand finance. It may belong to people who understand: Finance AI And the interaction between the two This is why I genuinely appreciate professionals like Akshat Shrivastava and Jayant Mundhra, who consistently simplify investing, markets, and #financial #literacy for all of us through their well-researched content. Because in the era of intelligent finance, #financial #literacy is no longer optional. It is becoming digital survival literacy. #AI #FinTech #UPI #FinancialLiteracy #ArtificialIntelligence #DigitalTransformation #GenZ #Millennials #IntelligentFinance #India #Leadership #FutureOfWork #AITransformation