Machine Learning in FinTech Striking Evolution, One Business at a Time

As I skimmed through machine learning headlines, I came across a common thread that’s binding almost all the present-day industries–Data analytics.

If you remove data from the startups and enterprises, they would seem hollow. Now that data is the asset, we have two ways to deal with data:

  • Let the data form a pile and rest in silos.
  • Harness data to predict future performance and opportunities.

While the top leaders in almost every segment have actively been leveraging data, businesses are arching their backs and being sceptical about its future–which we all know will be resolved soon.

If you belong to the sceptical group of businessmen, let’s get your doubt out of the window. Businesses are changing all the time. It is not because of products slash services but because of customers. So, in a way, companies don’t change. They adapt to the required changes.

The required change for the decade is machine learning that helps handle the data to gather insights for their next move. We don’t have to get into the definition of machine learning–you are tech-driven and smart. What we will get into is the application of machine learning in finance.

Machine learning in FinTech has long been on the periphery, but it is now coming towards the centre, meaning, more machine learning applications in FinTech are being explored.

The threats pertaining to data security and privacy always encircle the FinTech industry. However, machine learning is the shield to ensure privacy with value-added convenience for the internal processes.

Top Machine Learning Applications in FinTech

One of the done and dusted applications of machine learning in the FinTech industry is analysing the historical data to identify the patterns for making futuristic decisions. The application has been a success for numerous FinTech companies, as it moves downwards towards the small- and mid-sized FinTech businesses.

Even when a company operates in the finance sector, machine learning is the necessary leap towards FinTech. So, the critical question here is–how to use machine learning in FinTech?

Alternatively, what is the use of machine learning in FinTech?

To answer this question, I present top machine learning applications in FinTech below:

Asset Management and Portfolio Evaluation:

Investment isn’t a new concept. Despite not being widespread, there’s some common knowledge about it. Individuals, usually, in their late 20s spring into action for their future. However, not many have the required information about asset management, which pumps money for the brokers and mediators to thrive.

Recently, as individuals seek to manage their investments on their own, machine learning finds wide application in the asset management segment.

Machine Learning in FinTech for Asset Management:

  • Reduce the number of intermediaries.
  • Provide information to users about their assets.
  • Help hatch the high-performing assets.
  • Predict the future performance of assets.
  • Evaluate the users’ investment behavior and suggest improvements.

Evaluation of Insurance Policies:

The insurance companies operate on the level of the risk-taking abilities of individuals. When individuals take a risk, they pump in more funds than individuals that operate from their comfort. However, insurance companies need to classify these users based on their risk appetite. That’s where machine learning in the insurance sector can help analyze the investment pattern of customers.

As customers seek personalized insurance solutions, machine learning algorithms can assess customers’ profiles to understand their requirements and provide them with better policies.

Machine Learning in FinTech for Insurance Companies:

  • Calculate a person’s risk level.
  • Faster processing of a claim for users.
  • Evaluate fraudulent claims accurately.
  • Insurance advice to users

Loan Processing and Management:

Money lending applications and online businesses are growing with time. The once time-consuming loan approval process has been shortened, and speedy disbursement is possible. Some of the companies in the vicinity even claim to offer a loan as quickly as in the same hour.

Such a speed is possible with the machine learning capabilities, wherein the profile and applications of users are being uploaded on the portal and loan processing managers can draw insights from the system. Such software can track the credit score and point at any discrepancies to offer a prompt response on users’ loan application.

Machine Learning in FinTech for Loan Processing and Management:

  • Track financial habits of borrowers.
  • Evaluate credit card score of individuals.
  • Assess salary slips to evaluate risk-appetite of individuals.
  • Calculate time duration by when users can repay the loan comfortably.

Budgeting and Spend Trackers:

There are certain expenses that users can’t avoid. We categorize these as priority ones. House loans, car loans, electricity bills, food expenses, rents, EMIs, SIPs, etc., fall in the category of priority expenses. There are some secondary and tertiary expenses such as the subscription for that podcast or dinner at a fancy restaurant on the flip side.

Since users of today are turning more budget-conscious, consumers are finding solace in the FinTech applications to help them plan their practical monthly budgets. AI and machine learning in FinTech have proven to accelerate the industry’s growth pace. Artificial intelligence offers a responsive user experience that keeps users interested in the application.

Machine Learning in FinTech for Budgeting and Spend Tracking Applications:

  • Track the users’ spend behavior.
  • Estimate the income to expense ratio.
  • Calculate the total time taken to be liability-free from EMIs and loans.
  • Make suggestions on the spending for users to stay on track

Underpinning Transparency and Convenience

With so many fraudulent activities happening in the FinTech vicinity, if it is still running speedily with customers in the bandwagon, it is because it needs to run on the wheels of convenience and transparency. FinTech is no longer targeted to only the elderly generation. More millennials are showing interest in the finances. As a result, they seek convenience in understanding the unexplored areas of the finances. The FinTech industry is targeting the younger populace so that they can have a longer customer journey than the ones that are slightly or older. The FinTech industry is leveraging the combination of big data and machine learning technology to understand customers requirements and provide them with the necessary insights.
Machine Learning in FinTech for Underpinning Transparency & Convenience:

  • Equips users with the best of the industry knowledge
  • Helps them make better financial decisions.
  • Saves them from getting involved with fraud advisors.
  • Reduces the time required for getting financial information

Understanding Stock Market Better:

How many times have you come across individuals who want to make big money in the stock market?

Many times, right?

Those who are investing in the stock market can swear by the fact that you can’t master the skill of picking up the right stock at the right time in a couple of years. It is a consecutive process and that individuals need to keep themselves updated with the stock market’s working.

Exactly at this point in time, if a user gets the assistance of AI and machine learning in FinTech for understanding the stock market, the time required for learning reduces. This way, they can actively perform better in the stock market.

Machine Learning in FinTech for Understanding Stock Market Better:

  • Offer the knowledge of the past and present performance of a stock.
  • Predict the prices of stocks for the next month/quarter/year.
  • Assess the profit margin for a definite period of time.
  • Suggest best small, mid, and large-cap stocks for a specified budget every month

How Speedily Machine Learning FinTech Startups are Growing

We have compiled some data for you:

  • Around 8,705 startups & companies highlighted by Crunchbase are dependent on machine learning for their applications.
  • About 83% of the startups listed by Crunchbase that operate using machine learning have had less than three rounds of funding.
  • In the Q4, 2018, around 104 deals related to artificial intelligence were done, which rose to 116 in the Q1 of 2019.

Privacy and security concerns for the FinTech industry are the topmost priorities. In such a case, when machine learning can detect any disparities in the internal process at the time of occurrence, the FinTech industry stays invested.

The number of deals is a determiner of how far these companies are going with machine learning. From facilitating personalization to handling complexities and volume of data, machine learning has proven the benefits of offering business ease to startups and enterprises alike. In recent times, when the sources are manifolds, machine learning can consume high data volume and present them in actionable insights.

Of course, data associates and data analysts have been justifying their positions and work. However, the task undertaken by these professionals is, in a way much slower than the one that can be handled by machine learning capabilities.

Machine Learning: A Sophisticated Technology for Customer Management

When it comes to the commercial FinTech sectors such as banking, the competition is so high that they need to be prepared for the next step. In such a case, a lack of insights can only take more time to study the past and the present of the brands.

Below are some of the practical scenarios that can be applied to machine learning in FinTech to extract the best for the businesses:

Churn analysis

Churn analysis is nothing but the lifetime of a customer with a brand. User’s interaction with the application, in general, and the brand, in particular, can tell a lot about the customers’ experience. Besides this, how the user interacts with the brand also speaks highly about his loyalty.

Machine learning can provide accurate information about a customer’s satisfaction level so that the brands can make personalized deals and offer discounts to keep him from churning.

Lead Generation and Conversion

FinTech is a crucial industry, and it overlaps almost all other industries. This implies that individuals operating in other industries are the potential customers of FinTech. This unlocks a wide scope for the FinTech industry as a whole.

When machine learning in FinTech is integrated with the core business processes, businesses can track the leads and take the necessary actions to turn them into repeat customers. By gaining insights about customer behaviour, banking and insurance businesses can prioritize and restructure their resources to maximize the outcome of their efforts.

Customer Retention

More than the external processes, machine learning can prove to be effective for internal processes. When the businesses’ flaws and loopholes come to the fore, and more customers migrate towards a better product or service, machine learning in FinTech can help identify the patterns to reduce customer service agents’ reaction time.

Machine learning in FinTech can boost the customer satisfaction rate and help achieve optimum results as they are served with more personalized recommendations and products.

Key Takeaway

Even after voluminous knowledge regarding the incorporation of machine learning in FinTech, several businesses take a backseat to wait for the ‘right time’. Well, there’s no right time in business. By the time you wait for that right moment, what could have been your USP would have turned into your MVP in a blink of an eye.

Same goes with the latest technology. Machine learning applications in FinTech are sustainable ones, and we are yet to explore more grounds. Hence, the evolution of machine learning is happening at a rapid pace. So, it’s necessary to think and take actions about incorporating FinTech in your business ASAP.

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