Building AI for Neobanks and Financial Inclusion: Lessons from ArivalBank, SuitsMe, and Paycode

Two neobanks and a payment platform — three underserved markets

We’ve built multiple financial platforms that serve markets traditional banks largely ignore, and all of them illustrate how technology enables financial services that wouldn’t be economically viable without it.

ArivalBank is a digital banking platform regulated by Puerto Rico’s Office of the Commissioner of Financial Institutions (OCIF) as an International Financial Entity. It’s designed for global businesses that traditional banks consider too risky — companies in cryptocurrency, online gambling, cannabis (where legal), and other industries that mainstream banks reject outright. The platform offers multi-currency accounts, international payments, and dedicated account managers, all built on advanced encryption and a compliance-first architecture.

SuitsMe is a UK-based alternative banking service that bridges the financial gap for migrants and seasonal workers. In the UK, opening a traditional bank account requires proof of address and identity documentation that many migrants don’t have when they first arrive. SuitsMe streamlined the KYC process to serve this specific niche, growing to over 43,000 customers and facilitating £334 million in transactions through strategic partnerships with agencies that attract seasonal workers.

Paycode takes financial inclusion to its most fundamental level. In Africa, 1 billion people have no formal identity, 3.7 billion lack connectivity, and 1.7 billion remain unbanked. Paycode’s biometric digital identity and offline payment platform addresses all three barriers. Using tablet-based field registration, their teams create biometric identities and issue payment cards in deep rural areas — registering users in 5–7 minutes each. The platform works offline in real time, which is essential in regions with no connectivity. We’ve worked with Paycode on their technology, and the scale of impact is remarkable: 198,000 farmers onboarded in Zambia for a $22M subsidy programme in under 8 weeks, 18,000 social grant recipients in Mozambique, national payment switching infrastructure for the Bank of Ghana.

Why these platforms need AI more than traditional banks

Traditional banks have armies of compliance officers, relationship managers, and risk analysts. Neobanks — especially those serving underserved or high-risk markets — don’t have that luxury. They need to process the same regulatory requirements with a fraction of the staff, which means automation isn’t a nice-to-have. It’s the business model.

For ArivalBank, the challenge was KYC/AML for clients that other banks won’t touch. Enhanced due diligence for high-risk industries requires reviewing more documents, checking more databases, and applying more nuanced risk assessment than standard onboarding. Without AI-assisted document verification and risk scoring, the unit economics simply don’t work — the cost of manual compliance would exceed the revenue from each account.

For SuitsMe, the challenge was the opposite: simplifying KYC enough to serve customers with limited documentation while still meeting regulatory requirements. The AI layer had to be smart about which verification methods to apply based on the customer’s specific situation, rather than applying a one-size-fits-all process that would reject most applicants.

AI applications in neobanking

KYC automation is the most immediately impactful application. Document verification (is this passport real? does the photo match the selfie?), sanctions screening (checking against OFAC, UN, EU sanctions lists in real time), adverse media screening (checking news sources for negative information about the applicant), and risk scoring (combining all signals into a risk profile that determines whether enhanced due diligence is needed).

Transaction monitoring runs continuously after onboarding. The AI watches for patterns that suggest money laundering, fraud, or sanctions violations — unusual transaction sizes, unexpected jurisdictions, structuring patterns, rapid fund movement. For ArivalBank’s high-risk client base, this monitoring needs to be especially sensitive without generating so many false positives that the compliance team can’t keep up.

Customer intelligence helps neobanks understand their users and serve them better. Spending pattern analysis, cash flow prediction, personalised product recommendations — these are the features that differentiate a neobank from a plain prepaid card. For SuitsMe’s customer base, understanding seasonal income patterns (many customers are agricultural or hospitality workers with variable income) was important for building features that actually fit how they use money.

Architecture considerations specific to neobanks

Core banking integration is the foundation. Most neobanks don’t build their own banking core — they integrate with Banking-as-a-Service providers (Railsbank, Modulr, ClearBank, or similar). The AI layer sits between the customer-facing application and the banking core, processing decisions in real time.

Multi-currency support adds complexity for platforms like ArivalBank. Exchange rate monitoring, cross-border payment routing, and currency-specific compliance rules all need to be handled. AI can optimise payment routing (choosing the cheapest or fastest corridor for a given transfer) and flag cross-border transactions that require additional compliance checks.

Scalability matters more than you might expect. Neobanks can grow very quickly when they find product-market fit. SuitsMe’s growth through agency partnerships meant onboarding volumes could spike dramatically when a new partnership went live. The KYC system needed to handle these spikes without degrading the user experience or compromising compliance quality.

“Building for underserved markets taught us something that applies to all fintech development: the customers who need financial technology the most are often the ones that existing systems serve the worst. The technical challenge is adapting compliance and risk systems to serve these customers properly — not lowering the bar, but finding smarter ways to meet it.”

What we’d do differently today

If we were building these platforms today, we’d integrate LLMs earlier in the process — not for core financial decisions, but for compliance workflow assistance. Having an AI that can summarise a complex KYC case, draft a suspicious activity report, or explain a risk decision to a compliance officer in plain language would have saved significant time.

We’d also invest more in the data pipeline from day one. Both platforms generated valuable data about customer behaviour, transaction patterns, and risk signals. We used this data, but we could have been more systematic about building the feedback loops that make AI models improve over time.

---

Building a neobank or digital banking product? Contact us — we’ve built platforms for complex regulatory environments and underserved markets.