Finextra contributor details build requirements for prediction markets

Finextra has published an external opinion by India-based community member Anshul Saxena setting out the technical components operators need to run prediction-market platforms. The piece frames these venues as systems that turn trading on future events into live forecasts, with direct relevance for fintech firms handling payments, market data, settlement and regulatory risk.

Finextra identified the item as external content that was not edited by the publication and said it reflected the author’s views. Saxena’s Finextra profile lists him as based in India, with 18 opinions on the platform.

According to Saxena, a prediction market allows participants to buy and sell interests linked to the result of a future event, such as an election, a sports outcome or a product launch. Users who expect an outcome to occur can buy exposure that pays if the event resolves in their favour, while changing prices indicate the market’s assessment of probability.

Saxena distinguished that model from conventional betting by saying prediction markets are often positioned as tools for pooling dispersed information. The financial mechanism still requires a trading venue, user accounts, a settlement process and a method for deciding the final result.

Market design and pricing

The first component Saxena described is a market-creation engine. That system lets administrators or users define the event, list the potential outcomes, set the criteria for settlement and choose when trading closes. He said platforms may support yes-or-no contracts, multiple-outcome events or contracts tied to a numerical range.

Pricing can be handled through an order book or an automated market maker, according to Saxena. In an order book, buyers and sellers meet at agreed prices. In an automated market maker model, software adjusts prices according to available supply and demand, which Saxena said can help provide liquidity where trading activity is limited.

Wallet and payment infrastructure form another requirement. Saxena said centralized services may use conventional payment gateways, while decentralized platforms may connect cryptocurrency wallets and smart contracts. In both cases, the platform must record deposits, support trades and process withdrawals or payouts.

Settlement, data and operating risk

Resolution is a core operating issue, according to Saxena. Centralized platforms may use trusted administrators to determine outcomes, while blockchain-based services often rely on oracles, which are external data feeds that bring real-world results into smart contracts.

Saxena said unreliable or manipulated outcome data can undermine a market’s integrity. He also identified thin liquidity, regulatory compliance, scalability and security as technical challenges for developers. Prediction markets can touch gambling and financial-services rules in different jurisdictions, and Saxena said legal review is essential before launch.

The author contrasted centralized and decentralized structures. A centralized operator controls markets, funds and resolution, which Saxena said can make deployment faster and simpler while requiring users to trust the operator. A decentralized platform can use smart contracts for payouts and oracles for outcomes, offering more transparency while adding technical and regulatory complexity.

For implementation, Saxena listed Node.js, Python and Go as possible backend technologies, with React or Vue for user interfaces. He also cited PostgreSQL or MongoDB for data storage, Ethereum or Polygon for blockchain deployments, Solidity for smart contracts and WebSockets for real-time pricing and order updates.

This story draws on original reporting from Finextra Research.