Of the many industries being rapidly disrupted by AI, few are being more broadly impacted than financial services. From detecting fraud to improving investment forecasting, AI is bringing about widespread changes that allow financial institutions to meet the needs of their clients in more efficient, personalized and scalable ways.

However, AI’s most broadly available capabilities are really just the tip of the iceberg in unlocking the full potential of what financial services can become.

As Michael Heinrich, founder and CEO of 0G Labs, the first decentralized AI protocol (AIP), explains, scalable AI systems like DiLoCoX, in particular, offer significant potential for reshaping financial services.

DiLoCoX: The Foundation of Scalable AI

In Heinrich’s view, the full potential for scalable AI stems from DiLoCoX, a scalable system that enables large language models with over 100 billion parameters to be trained in a decentralized environment.

“DiLoCox combines local training with pipeline parallelism, a dual optimize policy, adaptive gradient compression schemes and one-step-delay communication overlaps to break learning models into multiple parts,” he explains.

“This allows multiple computers to train a piece of the model without overloading their memories. Even on network speeds of just one gigabyte, this approach allows for rapid scaling of an LLM without needing access to a major data center.”

In fact, one research analysis found that DiLoCoX training was up to 357 times faster than other decentralized LLM training methods. Such speeds, with only a minimal drop in accuracy, can be a powerful difference-maker in enabling firms to quickly develop, train and launch LLMs for internal use.

“Distributed systems make scalable AI available to a wide range of financial services providers — not just the biggest firms with the best financial technology resources,” Heinrich explains.

“Scalable AI can be trained on slower networks with 95% cheaper and more accessible hardware. This enables financial services providers to rapidly train their own LLMs, thereby enhancing their performance and delivering improved outcomes for their clients. With scalable AI, these kinds of results aren’t limited to the biggest firms. Widespread accessibility allows any firm to use AI-powered improvements.” 

Applying Scalable AI to Investment and Budgeting

As Heinrich explains, the first (and perhaps most obvious) application of scalable AI in financial services is giving smaller firms the ability to train their own predictive LLMs using their internal data.

“DiLoCoX allows financial firms to build sophisticated custom models that forecast market trends, investment opportunities and more that are tailored to their specific operations. By drawing on the firm’s own sales cycles, cash flow, risk profiles and historic performance, the proprietary LLM could deliver much more accurate forecasting than would be possible with a generic third-party tool. Ultimately, this can help firms make better financial decisions by ensuring they have the data and predictive analytics that are tailored to their specific situation.” 

Drawing from the firm’s own internal data, rather than relying on outside data sets, can help financial service providers better assess areas like client creditworthiness, which investment options best match a client’s risk profile or how a particular investment would affect the firm’s budget. 

While such LLMs can still draw on external data as part of their learning process, a focus on the firm’s existing activities and proprietary data helps ensure that forecasts and recommendations are better tailored to the firm’s unique needs and client base.

Further Improving Fraud Detection and Risk Management

Fraud detection and risk management have become even more of a priority in the AI age, with AI often being used by bad actors to facilitate fraudulent financial activities. AI-generated text, audio and video have been used to enact increasingly elaborate social engineering attacks, phishing schemes, investment fraud and other finance-focused attacks. A 2024 report determined that 42.5% of financial fraud attempts are driven by AI.

As AI becomes more prevalent in attacks in the financial services space, it also becomes an increasingly important tool in preventing such incidents. 

“While AI is already becoming a powerful tool in mitigating fraud, scalable AI takes those capabilities to the next level,” Heinrich says. “With scalable AI, financial institutions can train their LLMs on their own historical transaction data. Building a bigger model makes the AI more effective at identifying abnormal behaviors and transaction anomalies linked with fraudulent behavior. By detecting and acting on fraud earlier, financial institutions are better able to protect their assets and reduce losses.”

Heinrich also notes that the in-house development of these AI models further enhances security. While data security must continue to be a top priority, keeping all transactional data within a closed system lowers the cybersecurity and data privacy risks associated with third-party AI tools.

The Future Is Here

As Heinrich’s insights reveal, scalable AI represents a significant step forward in reshaping the future of financial services. By creating the opportunity for computers to learn locally and scale at speed, even without access to high-speed data centers, more financial service providers will be able to use AI to improve their offerings in meaningful ways.

Making scalable, custom-tailored LLMs more widely available will level the playing field and allow firms of all sizes to more effectively serve their clients and deliver dependable financial outcomes.