• Phone +1 (847) 483 8545
  • info@athenagt.com
machine learning finance athena

6 Ways Machine Learning Leveraged by Fintechs to Outshine Competition

by Team Athena on August 23, 2021 under Artificial Intelligence

Much before the advent of the banking apps and chatbots, machine learning was already in use in finance. However, the enormous growth in the volume of records and considering the quantitative nature of the financial sector, a much-developed machine learning technology was the call of the hour.

The old adage goes, ‘Give a man a fish and you feed him for a day; teach a man to fish and you feed him for a lifetime’.

Financial companies, the same way, have realized that it’s not enough to give instructions to computers. Instead, it’s time they started writing their own commands. Right from evaluating credit risks to beefing-up their own security, machine learning algorithms enable computers to work smarter than harder.

Fin Corps Confiding Upon Feature Engineering

Considering that 450+ leading financial institutions followed by 80+ expert speakers will be attending the upcoming October 2018 Machine Learning Fintech Conference, it is clear that fin corps, regulators, platform players, and investors have all their eyes set on deep learning technology.

machine learning finance

This brings us to an obvious question – Why financial companies are interested in investing in AI?

The answer is simple – With leading banks already leveraging this advanced technology along with the blockchain, fin corps are also willing to disrupt their businesses with the help of this platform.

The financial services industry is arguably the first to be directly affected by the ever-evolving technology. What computers did in the ’90s artificial intelligence is doing now. Machine learning, a branch of the AI, is capable of disrupting the financial services. Realizing its potential, the market spending on deep learning initiatives is estimated to reach USD47 billion by 2020.

Having said that let’s have a look at how this future-proof technology is transforming the financial sector. Continue reading!

6 widely used machine learning applications in finance

machine learning applications finance

#1 ML preventing fraud

The most concerning part for the financial service providers is security. They always face a hard time when it comes to protecting their clients from any kind of fraudulent activity. Every year, the American finance market faces a loss up to $50 billion. It’s time; technology put an end to this and ensured complete security. Gone are the days, when hackers managed to break into every data security. Machine learning is specifically designed to enable the network applications and thwart the hacking by out-thinking the hackers.

#2 ML providing better risk management

The artificial intelligence is undoubtedly a powerful ally when it comes to risk management. Traditional computers are only capable of predicting the integrity of the credit by extracting static data from records such as loan applications and financial reports. Machine learning, on the other hand, works smarter. For instance, it analyses the applicant’s monetary status, compares it with the current market value and on-going trends if required.

#3 ML predicting about investment

With the help of technology, computers have been placing orders on the behalf of investors when a stock reaches the desired price. Now by automating functions, deep learning platform makes the trading job easier by recommending the right kind of share to bait upon.

Talking about offshore investment funding, there has been a paradigm shift from the traditional predictive analysis to machine learning algorithms. The deep learning application is integrated to identify market changes earlier than the traditional investment models.

#4 ML providing optimum customer service

machine learning customer service

Apart from the fraud risk, poor customer service has been the major complaint among consumers ever since the inception of this sector. Thanks to deep learning and AI-led chatbots. The prevailing automated customer service is given thumbs down by many, as the customers feel this kind of service not helpful enough and irritating. The deep belief network enhances this automated customer support service. It does by so accessing data, recognizing patterns and interpreting human behavior.

One of the most important paybacks for companies that invest in machine-learning customer service is the ability to better understand the needs of each individual customer, which is always a good thing.

#5 ML as digital assistants

Every organization, big or small, needs a proper management which can efficiently function. The artificial intelligence platform provides solutions to this vertical as well. Deep learning is capable of helping executives perform their task seamlessly and faster than before.

We are here talking about digital assistants, where competition is fierce. Top players including Microsoft (Cortana), Google (Allo), Apple (Siri) and Facebook (M) have their own version of digital assistant, each providing the state-of-the-art virtual services.

The deep learning technology includes the following functionalities while developing a digital assistant –

  • Speech recognition
  • Access to big data
  • Enhanced data analytics capabilities
  • Co-ordination with social media, email, and third-party applications
  • Pattern recognition

#6 ML strengthening network security

The most important job of any data security officer is to recognize and identify any kind of suspicious patterns occurring across the network. Machine learning ensures no room of error when it comes to identifying such patterns. The power of AI-led pattern analysis combined with big data potentiality enables the machine learning technology to supersede the traditional network security tools.

Adding credence to the notion that machine learning is a viable network security tool, Microsoft is investing heavily in its own “deterministic” machine learning/big data platform. Using statistical analysis of baseline metrics, along with historical data from “bad” behaviors, computer scientists are constructing models that can identify “anomalous” or abnormal behaviors.

How Athena can help


To perceive all the possible aspects in which machine learning will impact the financial sector, the enthusiastic financial organizations will require the highly bankable – crystal ball. Realizing the potential of this technology in finance is not enough. Organizations will always need the help of an expert. As a digital transformation service provider, Athena enables the clients with innovative ways to leverage the deep learning algorithms.

Athena has the appropriate resources that come with expertise and experience to develop the state-of-the-art solutions apt for your business requirements. Whether you are looking for someone to help you in implementing a third-party ML application or a more sophisticated custom solution, Athena’s AI team provide you with a no-compromise solution at a lower cost than you might expect. Click here to contact us.

Leave a Reply

Your email address will not be published. Required fields are marked *