Role of AI in fintech industry explained

Considering the massive volume of financial transactions, manual recording of financial entries is highly dangerous.

With an increase in human-operated softwares, the possibilities of fraud, scams, and double-dealing always existed in the minds of business owners. 

Artificial intelligence in financial markets is the silver lining companies were eagerly looking forward to. 

Through AI-based softwares, the banking and financial sector can monitor transactions in real-time, create algorithms to identify possible scams, pinpoint unusual activities 24*7, notify the users immediately, and eradicate the chances of all digital frauds.

In the light of business instability, companies strive hard to stay afloat and maintain a steady pace of operations.

Banks and financial lending organizations saw an unpredicted wave of loan applications owing to the pandemic's deadly effect on the company's monetary well-being. 

Loan approvals have always been a tedious task for the banking industry; however, the loan approval process fell short of expectations with a sudden steer in loans and grant applications.

Keeping in mind the colossal processing time, the banking sector shifted towards AI softwares that enabled the online application of loans, meticulous scrutiny of loan documents, automated approvals for low-dollar sanctions, and assistance in high-value applications. 

The intelligent algorithms can track the client's financial history, extract credit score and repayment cycle, and a significant portion of predictive analysis.

Not to mention manual loan application screening has the possibility of human bias; however, with automated loan processing, this reduces drastically.

Customer satisfaction and retention are achieved by addressing and resolving their grievances on time.

With companies offering 24*7 customer support, employees need to work all weekdays, including the weekends, resulting in employee overload by addressing the simplest customer queries.

Delay in fixing their problem negatively impacts the company's goodwill and might create an unfavorable notion for its customer support. 

With the advancement in AI and machine learning, fintech companies have started using chatbots on their company website - that work and address customer problems based on back-end coding and algorithms.

They are programmed in a way that allows them to freely interact with customers based on the company's policies and guidelines.

These chatbots work their level best to fetch answers in their coding, but human intervention is still called for complex problems. 

Previously, companies never paid attention to customer data and how they could mold it to derive consumer behavior patterns because the market was never customer-focused.

Due to a lack of customer orientation and addressing their needs, companies lost chances to create consumer-centric products that cured their everyday problems. 

Eventually, when they began collecting customer insights, they were struck with manual grouping and data inconsistency. This made data analytics unreliable. 

With the advent of fast-paced technologies, companies no longer need to stick to their manual collection method.

AI-based softwares are competent enough to trace customer data with prior permissions and allow them to be analyzed to understand customer theory and thought-process.

The data derived is entirely free of errors and fallacy and is suitable for deciding long-term targeting and customer segmentation.

Additionally, decoding customer thought-process helps companies in catering to their needs and requirements by allowing them to introduce the product or features that a large portion of customers requires. 

The role of artificial intelligence in financial markets has expanded beyond the boundaries of the banking sector.

The stock market and blockchain companies have been active users of artificial intelligence and machine learning. 

The stock market fluctuations and patterns are difficult to predict.

With the arrival of algorithmic trading, companies have access to structured data sets and technical insights that transformed trading activities - which were challenging through statistical methods. 

With the recent development of AI-based trading platforms, users can download mobile applications of trading and investment sites and carry out their activities through the convenience of mobile.

There has been a 70-80 percent rise in algorithmic trading in the US stock market. 

Through AI, companies have a direct entryway to raw customer insights. But, access to data is one side of the coin, and the ability to analyze it is another side. 

Having lumpsum data is useless if companies are unable to analyze them.

With AI, companies can conduct predictive analysis, determining their current status quo and predicting how well they can stand up to future expectations.

Additionally, they can comprehend new revenue prospects, know their upsides, optimize asset utilization, conduct risk analysis, understand the production capacity and the number of personnel required, and more. 

However, every company has its own solutions based on its business model, size, operational efficiency, workforce strength, and other factors.

What might work for one company may not be the solution for another.

Hence, using leading-edge AI algorithms will yield different solutions for different companies. 

Fintech platforms have structured and secured coding that does not allow data to slip through the net.

No information is altered during the process. Therefore, companies obtain first-hand information about their transactions through the platform or app.

With real-time visibility, companies get an accurate image of the activities. 

The fintech tools are cost-effective options for businesses, unlike many who consider them high-cost alternatives.

The integration between the company's software and finance tools can yield substantial benefits without the hassle of installation costs.

Companies need not worry about physical storage and maintaining costs with everything digital.

Other reasons aside, the fintech tools and services are highly approachable, owing to their incredible speed and ease of use.

They do not need additional heavy storage extensions to use them.

For example, fintech payment solutions are readily available in mobile applications. 

With hardcore algorithms and multiple layers of coding, fintech apps are not easy to decode.

The customer data is highly protected with encryptions that may take years to be cracked.

While processing payments, your card details are not stored with third parties; instead, even the details are in encrypted form.

Finance companies always look forward to improving their workflows and becoming more robust.

Artificial intelligence in fintech is one such revolution that has reshaped the way companies see data and its related prospects.

With high adaptability among customers, these companies are sure of a stable future in finance.