Case studies
Real outcomes: challenge, approach, and results. Three examples from lending, ML in production, and technology-enabled hard lending.
Why case studies matter
When you are evaluating a software or consulting partner for lending, ML, or fintech, you want proof. Not just claims about capability, but concrete examples: what problem was solved, how it was solved, and what the results were. Case studies give you that. They show that the team has shipped similar work before, that they understand your domain, and that they can deliver on time and on budget. At AmpFi we work with banks, credit unions, and fintechs in five focus areas: AI and ML in banking, modernization, advisory and staffing, process automation, and discovery and strategy. The three case studies on this page are drawn from that work. Each one describes the client context (anonymized where appropriate), the challenge, our approach, and the results with metrics you can evaluate.
We believe in transparency. Each case study is written so that you can see what was built, how long it took (where we can share it), and what the outcomes were. We do not overstate results. The numbers we cite (default rates, precision, time to close, disbursement volume) are representative of what was achieved in those engagements. Your context will differ; the point is to show that we have done this kind of work before and that we can align on outcomes and deliver.
What we share and what we protect
Client confidentiality matters. In some case studies we can name the client or the product; in others we describe the context in general terms (e.g. a pan-African credit platform, a real estate and SME lender). We never share non-public financials, roadmaps, or internal decisions without permission. What we do share is the type of problem, the approach we took, and the outcomes that the client has agreed we can cite. That balance lets you assess our capability without compromising anyone’s competitive or regulatory position.
If you are a fit for our services, we can often arrange a reference call or a deeper dive with a client in a similar space. That happens after we have established mutual interest and usually under an NDA or a clear understanding of what can be shared. The case studies on this page are the first step: they give you enough to decide whether to reach out and what to ask when you do.
How to use these case studies
Read the one that is closest to your situation. If you are building or scaling a lending or BNPL platform, start with the AI-driven credit platform case. If you are putting ML into production for trading, risk, or decision systems, read the ML decision systems case. If you are in real estate or asset-backed lending and want digital application and AI-assisted underwriting, read the technology-enabled hard lending case. Each full case study is a long-form page with challenge, approach, results, and lessons. You can then bring specific questions to a discovery call: we will tell you what is similar, what is different, and what we would recommend for your context.
What you can expect from us
Like every engagement, we start by understanding your problem and constraints before proposing a solution. We align on scope, phases, and outcomes—discovery first when needed, then pilot or build with clear milestones. What we need from you: access to data, subject matter experts, and decisions when we hit trade-offs. Our engagement types page spells out typical bands and timelines.
Our team comes from fintech and lending. We have built and operated systems that process millions of dollars in loans and that run in production every day. We bring that experience to your project so you do not have to reinvent the wheel.
We are from the team behind a Techstars-backed fintech. We run lending; we build the software that powers it. That means we understand not only how to architect and ship systems but also how they are used in the real world: by operations teams, by risk, by compliance, and by the end customer. When we propose a solution, we are thinking about how it will perform in production, how it will be maintained, and how it will evolve as your business grows. That operator mindset is what sets us apart from pure-play dev shops that have never run a loan book or stood behind a decision engine in production.
Ready to see how we’d approach your initiative? Book a discovery call and we’ll outline what’s similar to these case studies and what we’d recommend for your context. No deck needed.
When to bring in a partner
Not every project needs an external partner. If you have a large in-house team, clear scope, and plenty of time, you may build everything yourself. But many banks, credit unions, and fintechs find that they are short on one or more of those: they have a small engineering team, scope is still fuzzy, or they need to move fast to hit a regulatory or market window. In those cases, a partner who has done similar work before can de-risk the project and shorten time to value. The right partner brings domain knowledge (so you do not have to teach them lending or ML from scratch), delivery discipline (so you get milestones and clarity, not open-ended engagements), and the ability to scale up or down as your needs change. We offer discovery and strategy engagements to align on scope, proof of concept and pilot engagements to validate an approach, and full build engagements when you are ready to go to production. We also offer retainer arrangements for ongoing support and iteration after launch. Our engagement types page describes the bands (e.g. discovery, pilot, build, retainer) and typical investment ranges so you know what to expect before we have a conversation.
Our five focus areas
The case studies above map to the kinds of work we do every day. We organize our services into five focus areas so you can quickly see where we add value. AI and ML in banking covers customer service automation (chatbots, personalization), back-office ML (underwriting, operations), and ML that actually ships in production. Modernization covers legacy and homegrown system upgrades, digital transformation without full rip and replace, and API and integration strategy. Advisory and staffing covers strategic advice and roadmap, embedded or dedicated technical capacity, and fintech-savvy talent. Process automation covers operations and workflow automation, compliance and reporting, and internal process digitization. Discovery and strategy covers problem validation and scope, build vs buy and architecture, and the path from discovery to delivery. Each of these has a dedicated service page on this site with more detail and outcomes. We recommend reading the case study that fits your situation and then exploring the relevant service page. If you are not sure which fits, a discovery call is the fastest way to align: we will ask about your goals and constraints and point you to the right place.
From case study to your project
Case studies show what we have delivered; the next step is to see how that maps to your project. We recommend reading the case study closest to your situation, then exploring the corresponding service page (e.g. AI and ML in banking for ML decision systems, or discovery and strategy if you are still defining the problem). Our how to choose a fintech development partner and how we scope lending and ML projects posts add more context on what to expect from a discovery call and phased delivery. If you are ready to talk, contact us or book a discovery call; we will tell you what is similar to these case studies, what is different, and what we would recommend for your context.
