Financial platform that automates key-decision making processes, with AI-based predictive modules that reflect the credit cycle. This financial platform is equipped with rich data-streaming, processing, and reporting capabilities to provide real-time, on-demand data.
This week’s U.S. fintech news shows how financial software is moving closer to controlled execution.
Across investing, digital banking, core banking, and payments, companies are investing in platforms that can connect decisions, controls, and execution. The competitive advantage is moving away from individual features toward the infrastructure that determines how financial actions are approved, executed, monitored, and audited.
For fintech teams, this means greater focus on data architecture, integrations, permissions, risk controls, and auditability. These capabilities are becoming the foundation for AI, embedded finance, and the next generation of financial products.
SoFi brings automated trading workflows into retail investing
SoFi is expanding its AI-powered investing strategy through the Composer deal. The move adds tools for creating, testing, and automating trading strategies, bringing more institutional-style investing workflows into a retail fintech product.
Source: www.thestar.com.
Why it matters
Retail investing products are expanding from portfolio management into strategy execution. As users gain tools to define rules, test scenarios, and automate investment decisions, the platform has to manage much more than transactions.
It needs to capture user intent, strategy rules, backtesting results, risk disclosures, consent, execution limits, and the full decision history. These capabilities determine whether automated investing can scale safely and meet customer, compliance, and operational requirements.
What teams should do
Teams building retail investing, wealth management, robo-advisory, or trading products should assess whether their platforms are ready to manage financial decisions, not just execute transactions.
What to review in your product
- Intent layer
Can the system capture the user’s goal, selected conditions, applied rules, and the reason behind each action, not just the final transaction?
- Simulation and backtesting
Can users validate a decision before money, assets, or data are put into motion?
- Execution controls
Does the platform support transaction limits, confirmation for critical actions, manual override, emergency stops, and permission boundaries for automated workflows?
- Audit trail and explainability
Can you reconstruct the complete decision chain, including input data, user request, applied rules, recommendations, warnings, user consent, and the final action?
- Consent infrastructure
Can the product manage consent separately for AI assistance, automated actions, and delegated authority?
- Event-driven architecture
Are decisions stored as events so the team can reconstruct and explain how the system reached a particular outcome?
Backbase Moves AI Agents Into Banking Workflows
Backbase acquired Kasisto, an AI platform used by banks for virtual assistants and conversational banking. The deal brings AI agents into Backbase’s Banking OS, where they can support customer service, sales, onboarding, and operational workflows.
Source: www.americanbanker.com
Why it matters
This deal shows where conversational banking is heading. AI agents are moving from the support interface into the operating layer of banking products.
Fintech and banking teams will increasingly compete on how quickly and safely a product can translate customer intent into a verified action. The bottleneck is moving away from chatbot interfaces toward backend orchestration: can the system understand a request, apply business rules, assess risk, obtain approvals, execute the operation, and retain evidence for compliance and audit purposes?
What teams should do
If your product supports banking operations, assess whether the platform is ready for AI agents beyond customer support. The next layer of competition will be about turning customer intent into verified actions across onboarding, servicing, sales, and internal workflows.
Start with five checks:
- Operating model
Check where AI can support operational tasks: service requests, onboarding steps, sales follow-ups, case routing, employee workflows, and customer support escalation.
- System connectivity
Review how easily AI can access and update data across CRM, core banking, KYC, payments, fraud, support, and document systems. Weak integrations will limit agents to answers instead of actions.
- Human handoff
Define when AI should pass a case to an employee, what context must travel with it, and how the employee can approve, adjust, or reject the next step.
- Operational visibility
Give teams a clear view of what AI handled, what it escalated, where it failed, and which workflows create the most manual work.
- Control ownership
Assign ownership for AI-driven workflows across product, risk, compliance, support, and operations. Agents will affect several teams at once, so controls cannot sit only inside the chatbot layer.
FIS helps First Commerce Bank prepare its core banking for the AI
First Commerce Bank selected FIS HORIZON to modernize its core banking platform. The bank plans to use the new infrastructure for API integrations, standardized data feeds, embedded finance, real-time payments, and future AI-driven services.
Source: www.fisglobal.com
Why it matters
Fintech products increasingly depend on the capabilities of the core banking platform: APIs, data quality, events, integrations, audit trails, and access controls. Without this foundation, AI, embedded finance, real-time payments, and partner services remain separate add-ons that are difficult to scale and launch safely in production.
For teams, this changes roadmap priorities. More attention will need to move from the customer interface to the infrastructure around core banking: data feeds, integration layers, permission models, event processing, and decision-checking mechanisms. This is where banks and fintech companies will determine how quickly they can launch new products, connect partners, and use AI in operational workflows.
What teams should do
Fintech platforms will increasingly compete on how quickly they adapt to new products, partners, regulations, and technologies. Check whether your architecture supports that pace.
- Product evolution
Check how quickly your team can launch a new financial product. If every new payment flow, lending product, or partner integration touches multiple core services, the platform will slow down. - Vendor independence
Keep business rules, customer experience, and operational logic outside third-party platforms. Core banking, KYC, fraud, payments, and AI providers should be replaceable. - Integration velocity
Measure how fast the product can absorb changes from banks, regulators, payment networks, AI providers, and partners. - Operational flexibility
Move pricing, limits, approvals, risk policies, onboarding rules, and product configuration out of hardcoded logic where possible. - Platform resilience
Design for provider failures, retries, fallbacks, partial availability, and switching between vendors. - Composability
Make payments, ledger, KYC, lending, fraud, notifications, AI, and reporting reusable across products, not tied to one customer journey.
Deluxe Expands Its Payments Business Through Celero
Deluxe is acquiring Celero Commerce for $625 million to expand its payments and data solutions business. The deal will add approximately 150,000 merchant locations, strengthen distribution through banks, ISVs, and ISO partners, and move Deluxe closer to becoming a top-10 non-bank merchant acquirer in the U.S.
Source: www.wsj.com
Why it matters
Fintech teams will need to design products around the full merchant workflow: payment acceptance, invoicing, reconciliation, reporting, partner distribution, access controls, and analytics.
This changes architecture requirements. Teams will need a unified data layer, APIs for ISVs and banking partners, event tracking, audit trails, and data monetization capabilities. Payments are becoming an entry point into a broader product that helps businesses manage sales, cash flow, and operations.
What teams should do
If you are building payments, merchant services, embedded finance, or vertical SaaS products, assess whether your platform can support merchant operations beyond payment acceptance. The next generation of fintech platforms will compete on how well they connect transactions, business software, data, reporting, and partner ecosystems.
- Merchant data layer
Check whether the product has a unified data layer across transactions, customers, invoices, settlements, fees, disputes, and cash flow. This data should support analytics, lending, forecasting, and automation without duplication across systems.
- Embedded payments
Design payment capabilities to work inside business software rather than as a separate destination. Payments should fit naturally into invoicing, subscriptions, commerce, field services, and other operational workflows.
- Partner infrastructure
Prepare the platform for banks, ISVs, ISO partners, and embedded finance providers. Build APIs, onboarding flows, permission models, tenant isolation, and configuration tools that allow partners to launch and scale quickly.
- Reconciliation and reporting
Treat reconciliation and reporting as core product capabilities. Merchants increasingly expect real-time visibility into settlements, fees, refunds, disputes, balances, and cash flow from a single interface.
- Event tracking
Capture every financial event across the transaction lifecycle, including authorizations, settlements, refunds, disputes, partner actions, and system decisions. This supports reporting, investigations, automation, and compliance.
Closing insight
Across investing, digital banking, core banking, and payments, product differentiation increasingly depends on operational architecture.
The companies in this week’s news invested in different parts of the fintech stack, but they are solving similar engineering problems: connecting data across systems, coordinating workflows, controlling automated actions, and producing evidence for customers, partners, and regulators.
For fintech teams, these capabilities increasingly determine how quickly new products, AI features, banking partnerships, and payment services can move from roadmap to production.
Product and engineering leaders can use one test: can the platform support these changes without redesigning core workflows every time a new partner, product, or automation layer is added?