Deep Learning Applications for FinTech Startups

Software for deep learning is increasingly being used in finance, as technology progresses in recent years. While there are more than 26,000 deep-learning fintech startups worldwide, not all available applications will help you serve your particular client base. 

Deep-learning fintech companies unlock the power of artificial intelligence to predict trends, and free up staff resources. These neural network processors can also help with risk assessment and fraud detection, among other uses. 

Let’s take a deeper dive into software for deep learning, and how it can be used in the financial industry. 

Making sense of big data

The finance industry has long relied on numbers to help clients make the best decisions. In the past several years, there has been an influx of the amount of data available thanks to advanced analytics. 

However, with big data comes a big headache to make sense of all the information. This is where neural network processors can really shine. Software for deep learning does more than share metrics. It analyzes the information, looking for patterns – even within unstructured data. 

AI has another advantage – it can learn, without the need for constant supervision from human teams. This is advantageous, as human analysts can sometimes insert biases into their findings that AI generally avoids. 

Better risk assessment and fraud detection

With deep learning software, fintech startups can minimize risk while detecting potential breaches more easily. 

Neural network processors can help assess a financial product or operation, and give an informed decision about whether it’s worth investing in. For example, AI can warn about potential losses on an investment earlier. 

Meanwhile, cyber crimes are on the rise – an investment fraud is the most costly among them. Stats show that on average, it cost affected companies more than $70,000 each. 

Having a smarter fraud detection program driven by AI can help deep-learning fintech companies offer more peace of mind to its staff and clients. 

On the subject of security, AI can also be used in identity management to verify investors (by comparing facial recognition data to records, as an example.) This reduces the risk of fraudsters impersonating their victims. 

Real-time investment/trading adjustments

Neural network processors never sleep (unless you ask them to.) This means the software for deep learning continuously analyzes new information in real-time, in order to make the best financial decisions. 

AI can take some of the pressure off your human analysts, performing complex calculations to maximize returns. 

Reduced operational costs, and streamlined processes

While software for deep learning can benefit investors through assessing risks and opportunities, it can also benefit the deep-learning fintech companies

Some repetitive tasks can be automated, freeing up your human staff to take on other tasks. You won’t have to worry about updating your systems as you scale, as deep learning software more easily adapt across an entire organization. 

They offer unique AI training applications, as well as AI inference (making financial predictions.) These tools are more efficient and better priced than competitors, and make more efficient use of power. They also allow easy migration from graphics processing units (GPUs.) 

To recap, the main ways neural network processors benefit deep learning fintech startups include:

  • Analyzing large sets of data that would take up resources to do manually
  • Helping customers make better investment decisions through predictions
  • Using AI to recognize potential security threats and take action
  • Reducing overhead costs while helping your human teams thrive 

Learn about the latest version of our inference software, and how advanced neural network processors can simplify and strengthen operations at deep-learning fintech companies

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