User Experience Design for Financial Services

Don't call customer service soft skills. This 3-part framework makes them just skills. 📚A quick history lesson before we dive in... The term "soft skills" likely originated with the U.S. Army in the 1960s. The Continental Army Command regulation 350-100-1 defined them this way: "job related skills involving actions affecting primarily people and paper, e.g., inspecting troops, supervising." Over time, "soft skills" have come to mean two things to trainers: 1. Interpersonal skills, like customer service 2. Vague skills that are hard to define or measure 🫤 It's the second part that hurts training. You can't consistently train or evaluate a skill that isn't clearly defined or measurable. In 1972, the Continental Army Command held a soft skills training conference to tackle this issue. Dr. Paul G. Whitmore from HumRRO (a contractor) presented a framework to make soft skills easier to evaluate: 1. What is the purpose of the skill? 2. What are typical situations where this skill is used? 3. What behaviors will successfully achieve the purpose? This framework works really well for customer service skills. 🤝 Let's use rapport as an example. The scenario is receptionists at a health club: 1. What is the purpose of building rapport with customers? ↳ Rapport creates a positive experience that encourages prospective members to join, encourages existing members to renew, and makes it easier to quickly solve problems. 2. What are typical situations where rapport is used? ↳ Examples where the health club receptions might use rapport skills include: ✅ Welcoming new and prospective members ✅ Greeting existing members ✅ Assisting members with membership-related issues 3. What specific rapport behaviors should receptionists exhibit? ↳ A few things might be on this list: (1) Use welcoming body language, such as a friendly wave and a smile. (2) Give visitor a friendly greeting such as "Welcome," "Good morning!", or "Hey (name of member)!" (3) Learn and use member names (4) Demonstrate an interest in the member Yes, this takes a bit more effort upfront to define each customer service skill. Here's the payoff: Clear expectations + consistent training + easy evaluation = Skills

Reducing Manufacturing Costs with GD&T: A Game-Changer for Engineers In the world of manufacturing, reducing costs without compromising quality is a constant challenge. One powerful tool that bridges the gap between design intent and cost efficiency is Geometric Dimensioning and Tolerancing (GD&T). Here's how GD&T helps reduce manufacturing costs: 1. Clear Communication: GD&T provides precise definitions of design requirements, eliminating ambiguity in engineering drawings. This ensures that all teams — from design to manufacturing — are aligned, reducing errors and rework. 2. Reduced Tolerance Stacking: By controlling geometric tolerances instead of relying solely on linear dimensions, GD&T minimizes overly tight tolerances. This reduces material waste, machining time, and inspection complexity, all of which lower costs. 3. Optimized Inspection: GD&T allows for easier and faster inspection using advanced tools like Coordinate Measuring Machines (CMM). This reduces the inspection cycle time and ensures products meet requirements without excessive testing. 4. Improved Assembly: Parts designed with GD&T fit together correctly the first time, reducing assembly issues and costly adjustments during production. 5. Flexibility in Manufacturing: GD&T allows manufacturers to use alternative processes or machines as long as they meet the geometric requirements. This flexibility leads to cost savings by utilizing available resources effectively. Why It Matters Incorporating GD&T into your design process isn’t just about technical precision; it’s about delivering cost-effective, high-quality products. For industries like aerospace, automotive, and medical devices, where precision is critical, GD&T is a competitive advantage. Are you leveraging GD&T in your processes? Share your experience or challenges in implementing it! Let’s discuss how we can use this tool to drive efficiency and innovation in manufacturing.

***Endogenous Liquidity Crises***   Why are financial markets so prone to liquidity crises and crashes? It is now well established that a large fraction of large price jumps (say, 4-σ events at the 1 min time scale, or major daily moves) cannot be explained by significant news. These jumps seem to be rather the result of endogenous feedback loops that lead to liquidity seizures. The memory of most spectacular ones is still vivid, such as the infamous S&P500 flash crash of May 6, 2010.   These events have triggered a large amount of controversy, in particular in the general press, pointing fingers at electronic markets and high frequency traders. However, financial markets have always been unstable. For example on May 28, 1962, the US stock market suffered a flash crash of severity similar to the that of May 6, 2010. This happened with good old market makers and, obviously, no HFT. Upon closer scrutiny one finds that the frequency of large price moves is remarkably stable over time, once rescaled by volatility.   A plausible general scenario is that of destabilising feedback loops resulting in "micro" liquidity breakdown. Consider for example the classic Glosten–Milgrom model relating liquidity to adverse selection. When liquidity providers believe that the quantity of information revealed by trades exceeds some threshold, there is no longer any value of the bid–ask spread that allows them to break even—liquidity vanishes! Whether real or perceived, the risk of adverse selection is detrimental to liquidity. This creates a clear amplification channel that can lead to liquidity crises.   Such a scenario, that was fleshed out and studied in a paper with Antoine Fosset (and more recently revisited by Guillaume Maitrier) is, we believe, at the heart of the excess volatility puzzle. Volatility is a high frequency, microstructural phenomenon that propagates to low frequencies – until price is a factor two away from value, at which point stabilizing, mean-reversion forces set in, exactly as anticipated by Fischer Black.      https://lnkd.in/ea467ceC Figure: alpha is the strength of the volatility/cancellation feedback, beta is the inverse memory time of the feedback. Red region: liquidity crises are inevitable. Blue region: stable order book dynamics.

🔍 Banks Are Missing Personalisation Clues Hiding in Plain Sight Every payment tells a story — who, what, where, and why. Yet most banks still treat payments as mere transactions, not as rich signals of customer intent. 💡 A cross-border tuition payment? ➡️ Could trigger FX hedging support, student insurance, or tailored credit offers. 💡 A recurring hospital payment overseas? ➡️ Might indicate a need for medical travel insurance or visa advisory. 💡 A supplier payment in a new country? ➡️ Signals expansion — prime time for trade finance onboarding or KYC refresh. 💡 An unexpected incoming payment from a crypto exchange? ➡️ Suggests the need for tailored tax advice or digital asset risk disclosures. 💡 Multiple small value international remittances? ➡️ May reflect a gig worker or freelancer profile — prime for invoice automation or FX cost optimization tools. 💡 A seasonal spike in local utility payments? ➡️ Could hint at vacation rental income — an opportunity to cross-sell SME tools or property-linked products. But here’s the catch: Most of this intelligence is locked in unstructured fields — lost in payment purpose text, truncated in legacy formats, or ignored entirely after settlement. That’s the missed opportunity. 🧠 The future of banking lies in interpreting payment intent, not just processing payments. Banks that build intelligence into their payment stack — with structured ISO 20022 data, AI, and contextual analytics — will be the ones to deliver true personalisation at scale. Ron Shevlin Nth Exception #payments #iso20022 #banking #data #ai #vc #crossborderpayments

If I'd need to do an AI for FP&A and Finance Roadmap in 2025 this is what I'd do: 1: Know the Possibilities of AI in Finance AI is not just ChatGPT — and it’s not just about answering questions. It’s about transforming how Finance operates. But it’s hard to know what you don’t know. That’s why the first step is awareness. Get familiar with what’s actually possible. Here are just a few examples of how AI can be used in Finance: ✅ Automated variance analysis – AI detects anomalies, highlights drivers, and explains them in seconds. ✅Forecasting & scenario planning – Build predictive models that adapt in real-time. ✅Expense & invoice classification – Automate tedious reconciliations and improve audit readiness. Before building your roadmap, open the window to what’s possible. 2. Choose an implementation partner and tool I have this resource with 30+ AI tools for Finance below But if you want to keep it simple, My top suggestion is OpenAI and ChatGPT If your company just uses Microsoft products, then explore Copilot If your company just uses Google products, then explore Gemini 3. Get your team trained on that tool No matter what LLM and AI company you choose to partner with, I think this is one of the most important steps. Every tool has its features The more you know about them, the more you can do with AI for Finance Some examples: GPTs from OpenAI: A game changer, you can add your policies, files and data in minutes and you can create a chatbot for your entire company Colab AI Agent from Google: Have an AI finance data scientist at your disposal to explore how to find the main drivers of profitability or do scenario modeling Copilot in Excel with Python from Microsoft: This can unlock data insights in seconds. My point is that every tool has its secrets. And you can spend hours and hours learning them. But AI changes every day. So instead of trying to keep up, choose a learning partner and get your team trained on use cases of AI in Finance. If you need help with that, let me know and I can give you suggestions Some options: AI Finance Club Self Paced Courses LinkedIn Learning Courses 4. Prioritise use cases Use my framework in the pdf Focus on Quick Wins and Major Projects Keep some Fill Ins ready Avoid Thankless Tasks 5. Create a Governance & Compliance Plan AI is powerful—but it needs guardrails. Define what data can and can’t be used Set standards for review and oversight This ensures your AI efforts are safe, ethical, and scalable. 6: Track and Share Wins Start small, but celebrate results. Did an AI tool reduce reporting time by 50%? Did automation save your analysts 10 hours a week? Share it with the team and leadership. Build an Internal prompt Library and start documenting every AI idea or request that comes up—big or small. Momentum builds when people see the value. If you need the full version of this guide (20+ pages), comment and I'll send!

If your “CRM transformation” still relies on end-of-month heroics and a spreadsheet called FINAL_v9_REALLYFINAL.xlsx, you’re not using your system—you’re working around it. Sustainable growth in a service business isn’t a software problem; it’s an adoption problem. Tools don’t fail—behaviour, process and governance do. Here’s where service businesses go wrong: 1. Buy the tool, don’t change the behaviour. The team keeps side docs; the CRM gathers dust. 2. Data capture is optional. Free-text chaos, no required fields, inconsistent stages = fantasy forecasts. 3. No clear ownership. No RevOps, no data stewards, no definitions everyone obeys. 4. Activity lives outside the CRM. Emails aren’t logged, calls aren’t tracked, “we’ll update it later” never happens. Adoption architecture (People → Process → Platform): 🔵 People: Train continuously. Explain the why (fewer status meetings, faster invoicing, cleaner pipeline). Create champions. Tie compliance to outcomes (yes, compensation if needed). 🔵 Process: Define lifecycle and stage gates everyone follows. Make key properties required at the right moments. Set SLAs, audit regularly, and stop progressing deals without the data. 🔵 Platform (HubSpot done properly): Required fields by stage; Playbooks for structured discovery; native email/calendar logging; task nudges (“no activity / update in 14 days—update or move on”); adoption dashboards (property completeness, deal hygiene, SLA hits/misses, user activity). This is just the basics—then you get to the clever stuff: guided selling, capacity-aware routing, renewal risk scoring, margin alerts at scoping… all of which only work if the foundations are solid. Takeaway: If you’re still blaming the tool, you don’t have a platform—you have a process problem. Adoption is a choice. #crmtransformation #datatransformation #hubspotpartner #professionalservices #revops #reportingdata #businessgrowth #growthenablement

The hardest part of being a salesperson? Not closing deals? Not handling objections? It’s updating the CRM 😅 We’ve all been there. But your CRM is only as good as the data you put into it. If it feels like a chore, it’s time to make it work for your team, not against them. Here’s how: 1️⃣ Simplify the process Too many fields or unnecessary steps? Cut them. Keep it lean so your team can focus on selling, not admin work. 2️⃣ Automate data entry Use tools like email tracking, call logging, and activity sync to handle the basics. Less manual input = happier reps. 3️⃣ Make it useful for reps If your CRM feels like it’s only for managers, no one will care. Show reps how it helps them prioritise leads, track follow-ups, and close more deals. 4️⃣ Provide proper training Don’t assume everyone knows how to use the CRM effectively. Run training sessions to show shortcuts, best practices, and how it fits into their workflow. 5️⃣ Reward good habits Recognise and reward the reps who consistently keep the CRM updated. Positive reinforcement goes a long way. 6️⃣ Use data to sell smarter Make the insights visible and actionable. Show your team how CRM data can uncover trends, highlight hot leads, and predict customer needs. 7️⃣ Integrate CRM with other tools Make it seamless. Connect your CRM to email, calendars, and project management tools to reduce context switching and manual effort. 8️⃣ Set the tone from leadership If managers aren’t updating the CRM, reps won’t either. Lead by example and make it part of the team’s culture. 9️⃣ Limit duplicate data entry Nothing frustrates a salesperson more than entering the same information in multiple places. Streamline your systems to avoid redundancy. 1️⃣0️⃣ Review and refine regularly Your CRM setup isn’t set in stone. Get feedback from your team and adjust workflows, fields, and tools to make it more effective over time. Updating the CRM doesn’t have to be the hardest part of the job. A few tweaks can turn it into a tool your sales team wants to use. What’s your team’s biggest CRM challenge and how have you solved it?

You can run the best campaigns in the world. But if your CRM is disconnected, you’ll never see where the money is leaking. Here’s what happens: Leads come in from your paid ads. But they never make it into the CRM properly. Maybe they get stuck in a spreadsheet. Maybe they flow into the system, but without clear attribution... Or, even worse, they land in your CRM — and just sit there with no follow-up. Either way, you end up with a pipeline full of ghosts. Your close rate drops. Your marketing spend goes up. And nobody can explain where the gap is. The real issue isn’t the ads. It’s the disconnect between your marketing tools and your CRM. When systems aren’t speaking to each other, you’re not just wasting budget — you’re missing opportunities every single day. The fix is simple: 1) Connect your tools properly: Make sure every lead flows straight into your CRM, enriched and ready for sales. 2) Automate handoffs between marketing and sales: So follow-ups happen fast. 3) Build clear attribution so you know exactly what’s driving pipeline. If your CRM isn’t tracking every dollar you spend, it’s draining your budget - whether you see it or not. Tighten the flow, tighten the results. If this is happening in your system, shoot me a message Jordan Nelson I’ll show you how to plug the holes before your next campaign goes live.

After optimizing costs for many AI systems, I've developed a systematic approach that consistently delivers cost reductions of 60-80%. Here's my playbook, in order of least to most effort: Step 1: Optimizing Inference Throughput Start here for the biggest wins with least effort. Enabling caching (LiteLLM (YC W23), Zilliz) and strategic batch processing can reduce costs by a lot with very little effort. I have seen teams cut costs by half simply by implementing caching and batching requests that don't require real-time results. Step 2: Maximizing Token Efficiency This can give you an additional 50% cost savings. Prompt engineering, automated compression (ScaleDown), and structured outputs can cut token usage without sacrificing quality. Small changes in how you craft prompts can lead to massive savings at scale. Step 3: Model Orchestration Use routers and cascades to send prompts to the cheapest and most effective model for that prompt (OpenRouter, Martian). Why use GPT-4 for simple classification when GPT-3.5 will do? Smart routing ensures you're not overpaying for intelligence you don't need. Step 4: Self-Hosting I only suggest self-hosting for teams at scale because of the complexities involved. This requires more technical investment upfront but pays dividends for high-volume applications. The key is tackling these layers systematically. Most teams jump straight to self-hosting or model switching, but the real savings come from optimizing throughput and token efficiency first. What's your experience with AI cost optimization?

𝗠𝗼𝘀𝘁 𝗕𝗮𝗻𝗸𝘀 𝗔𝗿𝗲 𝗙𝗮𝗸𝗶𝗻𝗴 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻. 𝗛𝗲𝗿𝗲'𝘀 𝗪𝗵𝗮𝘁 𝗥𝗲𝗮𝗹 𝗟𝗼𝗼𝗸𝘀 𝗟𝗶𝗸𝗲 "We need to personalize more" — every bank says it. But here’s what I actually see: → 𝟴 𝘁𝗼 𝟭𝟬 𝗳𝗶𝘅𝗲𝗱 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝘀 → CRM campaigns sent in monthly batches → No real-time scoring or continuous testing That’s not hyper-personalization. That’s segmentation with lipstick. According to BCG, real hyper-personalization looks like: → Real-time AI decisioning → Always-on data feedback loops → 1:1 experiences across journeys—not just channels I’ve worked with teams who want this—but can’t get past outdated CRMs, monthly campaign cycles, or siloed teams. This isn’t a marketing tweak. It’s an architectural, operational, and cultural shift. Follow 𝗙𝗿𝗶𝗲𝗻𝗱𝗹𝘆 𝗡𝗲𝗶𝗴𝗵𝗯𝗼𝗿𝗵𝗼𝗼𝗱 𝗚𝗼𝗸𝘂𝗹 for insights at the intersection of IT, architecture, and enterprise platforms. #PersonalizedBanking #AIinBanking #TechwithGokul #FriendlyNeighborhoodGokul