Harvey, a legal AI startup focused on financial services contracts, raised $80 million in its Series B in early 2024 at a valuation exceeding $700 million. The company was less than two years old. Its product uses generative AI to review, draft, and analyse financial contracts, performing in minutes work that previously required teams of lawyers billing at $500 to $1,500 per hour. Harvey’s growth illustrates a pattern across fintech: the next generation of AI-powered platforms is not being built by adding AI to existing financial products. It is being built by starting with AI capabilities and wrapping financial products around them.
The market for these next-generation platforms is expanding rapidly. According to MarketsandMarkets, the global AI in finance market reached $38.36 billion in 2024 and is projected to grow to $190.33 billion by 2030. Grand View Research estimates that generative AI in financial services specifically will grow from $2.21 billion in 2024 to $25.71 billion by 2033. These figures capture the first generation of AI in finance. The next generation, built on AI agents, multimodal models, and autonomous financial operations, will be significantly larger.
What Distinguishes Next-Generation Platforms
The current generation of AI-powered fintech platforms uses AI to improve existing processes: better fraud detection, faster credit decisions, more efficient customer service. The next generation uses AI to create entirely new categories of financial capability. Three characteristics distinguish them.
According to Mordor Intelligence, the AI in fintech market is projected to grow at a compound annual growth rate exceeding 20 percent through 2029, driven by demand for automated fraud detection, credit scoring, and customer service applications.
Research from McKinsey’s 2024 analysis indicates that organisations deploying AI at scale report efficiency improvements of 15 to 25 percent within the first 18 months of production implementation.
AI-first architecture. Current fintech companies were built as software platforms that added AI. Next-generation platforms are built as AI systems that deliver financial products. The difference is architectural. An AI-first platform designs its data model, infrastructure, and product logic around what AI models need to function optimally, not around traditional banking workflows. Ramp was not built as a corporate card company that added expense analysis. It was built as an AI spending intelligence platform that uses a corporate card as the data collection mechanism.
Autonomous operation. Current platforms recommend actions for human approval. Next-generation platforms will execute multi-step financial operations independently within defined parameters. An autonomous treasury management system will not just predict cash flow. It will move money between accounts, execute FX hedges, and adjust credit facilities based on predicted needs, reporting results to the CFO rather than waiting for approval at each step.
Cross-functional intelligence. Current AI applications in finance tend to be siloed: one model for fraud, another for credit, another for customer service. Next-generation platforms integrate intelligence across functions so that insights from one domain improve decisions in another. A platform that notices a customer’s income has increased (from transaction monitoring) can simultaneously adjust their credit limit (underwriting), modify their investment allocation (wealth management), and update their insurance coverage (risk assessment), all from a single data signal.
Five Platforms Defining the Next Generation
Several companies are already building next-generation AI-powered fintech platforms. Each approaches the opportunity from a different angle.
Ramp: AI-powered corporate finance. Ramp’s corporate expense platform has evolved from a spend management tool into an AI-driven CFO assistant. The system automatically identifies duplicate software subscriptions ($150 million found in 2023), negotiates better vendor rates, enforces spending policies, categorises expenses, and generates financial reports. Ramp’s next phase extends this intelligence to budget planning, vendor management, and financial forecasting. The platform’s advantage is that it sits on corporate spending data from thousands of companies, giving its AI models training data that improves with every customer.
Harvey: AI-native financial legal services. Harvey’s platform processes financial contracts, regulatory filings, and compliance documents using large language models fine-tuned on legal and financial data. Law firms and financial institutions use it to review contracts in minutes rather than days, identify non-standard clauses, and generate first drafts of financial agreements. The platform represents a new category: AI that performs the analytical work of highly paid professionals at a fraction of the cost and a multiple of the speed.
Addepar: AI-powered wealth intelligence. Addepar manages data and analytics for over $5 trillion in wealth management assets. Its platform aggregates portfolio data from hundreds of custodians and data sources, normalises it, and provides AI-driven analytics. The next-generation features include natural language portfolio queries (advisors asking questions about client portfolios in plain English), automated reporting, and AI-generated investment insights. The platform is becoming an intelligence layer between wealth managers and their clients’ financial data.
Brex: AI-driven business financial operations. Brex started as a corporate card company and has evolved into an AI-powered platform for business financial operations. Its AI sets dynamic credit limits based on real-time cash flow, automates expense reporting, generates financial forecasts, and manages vendor payments. The platform is building toward a model where AI handles the routine financial operations of a startup or mid-market company, from bill payment to budget tracking to compliance, with human oversight focused on strategic decisions.
Column: AI-native banking infrastructure. Column is a bank holding company that provides banking infrastructure through APIs. Unlike Banking-as-a-Service providers that sit on top of partner banks, Column holds its own bank charter. Its platform combines direct banking capabilities with AI-powered decisioning, allowing fintech companies to build products that use AI for credit, fraud, and compliance decisions natively rather than bolting AI onto legacy banking infrastructure. The AI-native banking layer enables fintech companies to launch products faster because the AI infrastructure is built into the banking platform itself.
The Technology Stack Behind Next-Generation Platforms
Next-generation AI fintech platforms share common technology characteristics that distinguish them from current platforms.
Foundation models provide the base intelligence layer. Rather than building every model from scratch, these platforms use pre-trained large language models (from OpenAI, Anthropic, or Google) as a foundation and fine-tune them on financial data. This approach provides capabilities (natural language understanding, document analysis, code generation) that would take years to build independently.
Vector databases store and retrieve unstructured financial data (contracts, research reports, regulatory filings) in a format that AI models can query efficiently. This technology enables features like “search through all our client contracts for non-standard termination clauses,” a capability that was impractical before vector search made unstructured data queryable at scale.
Real-time feature stores provide machine learning models with pre-computed features (variables derived from raw data) at inference time. A credit model that needs to evaluate a loan application requires hundreds of features computed from the applicant’s transaction history, employment data, and external signals. Feature stores compute these features in advance and deliver them to models in milliseconds, enabling real-time decisions on complex data.
Agent frameworks orchestrate multi-step AI workflows. When an AI system needs to check a customer’s identity, evaluate their creditworthiness, generate a loan agreement, and initiate fund transfer, an agent framework coordinates these steps, handles errors, and ensures the process completes correctly. Agent frameworks are what enable the autonomous operation that distinguishes next-generation platforms from current ones.
What Comes After
The trajectory of AI-powered fintech platforms points toward a specific future: financial services that are personalised to each individual, automated in execution, and intelligent in adaptation.
A consumer’s financial life in this future is managed by an AI system that monitors all their accounts, optimises savings, manages investments, adjusts insurance coverage, handles tax obligations, and makes payments, intervening only when a decision exceeds its autonomous authority. The consumer sets goals and boundaries. The AI handles operations.
A business’s financial operations in this future are managed by an AI platform that handles payroll, vendor payments, tax compliance, cash management, financial reporting, and audit preparation. The CFO sets strategy. The AI executes.
These are not speculative scenarios. Every component required to build them exists today. Wealthfront already manages investments autonomously. Ramp already manages corporate expenses autonomously. Lemonade already processes insurance claims autonomously. The next-generation platforms combine these autonomous capabilities across financial domains and extend them with each new AI advancement.
The fintech companies building these platforms today have a window of perhaps three to five years before the market consolidates. The platforms that accumulate the most data, train the best models, and build the deepest customer relationships during this window will be the ones that define financial services for the next generation. The rest will be their customers.
