Beyond fintech: How AI is changing the finance landscape for everyone

Learn how forward-thinking financial institutions are using AI and machine learning to enhance the customer experience and improve everyday financial processes.

Intelligent finance is now

The financial services industry is undergoing a massive transformation. According to Gartner, traditional financial services firms face extinction by 2030 if they fail to transform their business models and respond to FinTech disruption.

But this is an industry heavily reliant on speed – and transformation takes time. That’s why the smartest traditional firms are turning to artificial intelligence (AI) to welcome a new era of accelerated financial services.

Through a combination of intelligent analytics, automation, and real-time data insights, firms can react to change faster, surpass customer expectations, and make smarter, more accurate business decisions.

In this blog, we’ll explore the five key processes and workflows being transformed by AI today.

Assess customer eligibility

1. Assess customer eligibility

From renting property to buying a car and even gaining employment, your credit score can dictate where and how you live. But as many millennials will tell you, not everyone has been well served by the credit decision-making process.

That’s why banks and lenders are now using AI and machine learning to assess large datasets to make faster, more accurate and well-rounded judgements – including for people with little or no credit history.

Additionally, AI helps lenders better identify risky applications and reduce losses due to missed payments. For example, a top US auto lender was able to cut its losses by 23% annually using machine learning to win more reliable borrowers.

Improve fraud detection

2. Improving fraud detection

While machine learning is helping firms reduce lending risks, it’s also helping them take a more proactive, data-driven defense against fraud.

By processing and analyzing terabytes of transaction data, firms can train machine learning models to better distinguish between real customers, scammers, and even bots. Firms are also using AI to proactively trawl networks and detect suspicious activity before damage is caused.

As these processes are automated, fraud prevention teams are freed up to spend more time resolving legitimate instances of fraud and reduce time spent chasing false positives.

Enhance quantitative trading

3. Enhancing quantitative trading

Quantitative trading is a sophisticated process, but one that AI can help simplify.

At its core, quantitative trading uses large amounts of market data to identify repeatable patterns that can be used strategically when trading. For example, analyzing price patterns to identify the best performing stocks.

Increasingly, quantitative analysts, employed for their understanding of complex mathematical models, use AI to accelerate the analysis of large datasets and even automate routine trading processes to stay one step ahead of the market.

Deeper market analysis and forecasting

4. Performing deeper market analysis and forecasting

Forecast accuracy can make or break a firm’s reputation – and seriously impact its bottom line. To better anticipate customer needs and improve forecasting, many financial institutions are using machine learning and AI to:

  • Predict market trends before they happen
  • Answer complex financial questions
  • Automate routine analytical processes

For example, an AI-powered algorithm trained by an Israel-based stock forecast company was able to demonstrate 97% accuracy in its predictions for the S&P 500 and Nasdaq indices. To achieve these results, the algorithm was fed 15 years of trading data, with new data added every day to ensure the latest insights.

Personalize and automate retail banking

5. Personalizing and automating retail banking

With the emergence of connected, personalized experiences on smart devices, customer retail expectations are rapidly shifting.

In the retail world, brick-and-mortar retailers are scrambling to unify physical and digital engagement, with customer data being used to help create powerful, personalized experiences – online and off.

Retail banking is no different. For example, loan advisors can use AI-powered insights to personalize products and enhance customer relationships.

Chatbots and advanced voice assistants can also help customers self serve and get secure, context-aware financial advice without leaving home. Think real-time budgeting advice, definable spending boundaries, and proactive fraud remediation.

And with the right cloud tools and GPU capabilities, banks can even deploy seamless, cross-channel assistants – freeing valuable manpower while extending support.

The future of finance needs power

These are just a few ways financial institutions are using AI and machine learning to enhance both customer experience and internal operations. To enable innovation across your own institution, you need power – the kind you get from the partnership of NVIDIA and Google Cloud. Or what we like to refer to as the Power of Two.

With NVIDIA’s industry-leading GPUs running on a flexible cloud platform, we can help you seize the future of accelerated financial services without disrupting your everyday processes or paying too much for more expensive, on-premises technology.

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