How Machine Learning Is Augmenting The FinTech Industry

The evolution of artificial intelligence and machine learning applications in FinTech (financial technology) has the potential to transform the financial industry at a pace. As modern technology advances, it strengthens new business models to employ modern data analytics techniques to make more efficient business decisions at a higher speed and lower cost. The exceptional benefits of machine learning to transform and scale operations, financial analysis systems, and customer engagement practices.

Machine Learning Applications in FinTech

The modern financial industry is driven by innovation. Using AI and machine learning has facilitated financial and banking operations. The recent developments in technology deliver end-to-end solutions and services as per the needs of the customer. According to a Forrester report, 50% of the banking and financial services around the globe are leveraging AI and Machine Learning technology advantage.

The mountain of AI and machine learning opportunities saves FinTech firms time by reducing human errors at the core. And in this article, we have listed a few prominent use cases below.

1. Algorithmic Trading

Algorithmic trading is a new-age technique revitalizing the trading market sphere. The actual benefits of algorithmic trading are not realized yet. And it will be a prominent tool to survive tomorrow’s financial markets dealing with automation.

 It is considered that 80% of decisions related to stocks are made using algorithm trading. Algorithm trading or Algo trading refers to leveraging machine learning models to perform predictive analytics and advanced mathematical models to predict future price fluctuations and make accurate and high-speed trading decisions.

2. Accurate Decision-Making

Machine learning technologies can analyze vast data sets more accurately at a faster speed. This technique is frequently practicable and can be leveraged to make end-to-end decisions to scale banking and financial services, automate business processes, and increase customer outcomes with less investment of time and cost.

3. Fraud Identification and Prevention

Greater volume and variety of data accelerate the business decision-making process. Leveraging Machine Learning anomaly identification and prevention can automate; how banking and financial businesses identify fraud transactions or banking actions and prevent these actions from causing financial losses.

Machine learning technologies can identify complex, nonlinear patterns in large volumes of data and helps to create models with higher accuracy. These data models also self-learn every bit of every data and pattern to improve their predictive power with time. 

4. Better Customer support

Utmost customer outcomes are a delight to businesses to scale their growth and generate more revenue. Automated customer support qualifies the needs of the customers but providing customized solutions. For instance, an AI-powered chatbot is one of the applications in banking that elevates the rate of interaction per minute by decreasing customer waiting time. The rule-based chatbot operates on a particular command and an AI-based chatbot gets smarter with every interaction.

5. Client Risk profile

In the developing world, fintech companies must categorize clients by data analysis, and allied patterns. AI tools show excellent capabilities in it to automate the process of profiling clients, based on their risk profile. This profiling work helps experts give product recommendations to customers in an appropriate and automated way.

6. Beyond Predictive Analytics

 Machine learning algorithms helped organizations think beyond traditional predictive analytics applications. Financial specialists can identify future opportunities, predict market trends, and takedown fraud at the earliest than traditional analytics solutions. Machine learning can also be applied to early warning systems. ML-enhanced early warning systems can be used by banks and other financial institutions to predict anomalies, reduce risk cases, monitor portfolios, and provide recommendations on what to do in cases of fraud.

The application of AI & Machine Learning in banking is more than a trend today. Taking into account all use cases provided above clear that machine learning and AI play prominent roles in the Fintech Industry, and going on, the evolution of technology shows more potential to transform and automate this industry. As banks undergo digital transformation and customer preferences evolve, interpreting data and taking advantage of insights generated is a necessity today.

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