How to improve operational efficiency in Fintech with Big Data Analytics, AI and ML

Sophie Smith

Head of Marketing, Spyrosoft Ltd.

Improving fintech operational efficiency and taking it to the desired level requires a multi-dimensional and holistic approach on the part of all financial institutions, including fintech companies. Reducing costs, digitalisation and automating processes, improving security standards and optimising and diversifying services, are some of the critical aspects which require consistent operational efficiency 

It is equally crucial that innovation and traditional methods are combined to create highly effective operational practices to enhance productivity and maximise profits.  

In this era of rapid developments in quality and methods in financial services, Big Data Analytics, along with AI and ML, have emerged as potent tools for creating innovative business models. Riding on this wave of change, fintech companies are rendering high-quality and seamless services to customers at surprisingly low prices.  

For example, Customer Sentiment Analysis facilitated by Big Data analytics tools has allowed fintech companies to make their offerings more relevant and viable for different customer groups.

Let’s take a closer look at how fintech companies can effectively improve their operational efficiency using the technologies mentioned above.

How to measure and enhance operational efficiency?

The basic variables in the operational efficiency calculation are the operational expenses versus profits. In general, the lower the costs in comparison to profits, the greater the operational efficiency.

The key to enhancing operational efficiency is to optimize the processes, and thus lower the operational expenditure.

Gaining insight into the behaviour patterns of consumer groups, accuracy in customer segmentation, the optimum analysis of macroeconomic trends, enhancements in service delivery methods and customisation of services are some of the factors leading to better operational efficiency. Effective risk management strategies, that are compliant with regulatory measures, are also essential.

It is vital that all the above-stated objectives are fulfilled at reasonable costs to ensure profits are kept in line with business goals.

Challenges faced by fintech companies in improving operational efficiency 

Operational efficiency for fintech companies is a general set of objectives with multiple areas of consideration. As a result, the challenges and problems they face when enhancing operational efficiency relate to specific processes in their operations and functionality 

Areas where optimum operational efficiency is crucial, along with the existing issues, have been described below: 

Optimising processes

Fintech companies need to automate and optimise all relevant processes of their operations to attain overall goals of operational efficiency. The modern landscape mandates automation wherever possible to ensure flawless processes are completed in minimal time.  

Risk management solutions

With emerging cyberthreats, an increase in instances of fraud and the evolving nature of macroeconomic trends, fintech companies need to use the highest standards in risk management. Failure to use AI, ML and Big Data Analytics will make it impossible for them identify risks such as fraud, money laundering and malware attacks. Inability to detect risks at the earliest possible stage also means that they can’t take appropriate preventive or corrective actions. 

Complying with regulatory measures

The regulatory obligations across nations are increasing. However, they haven’t been able to kept pace with the changing trends in the delivery of financial services using the latest technology. The operational efficiency of fintech companies can be negatively affected if they fail to comply with mandatory statutes in force in all regions where they operate or provide their services.  

Improving service quality and customisation of services 

To grow at the desired speed, fintech companies need to continuously improve their services to meet the demands of their customers. The analysis of voluminous datasets about target customer groups can provide valuable insights into the desires and expectations of individuals as well as corporations. If fintech companies fail to meet the required standards for improving service quality and customisation, the goals of operational efficiency can’t be achieved.  

Management of expenses

All the individual aspects of enhancing operational efficiency need to be managed and implemented at minimum costs but at maximum efficiencyTo optimisoperations, fintech companies need to make sure they come up with excellent methods with low investment where possible

What's fintech companies and financial institutions approach towards AI, ML, and Big Data?

International Data Corporation (IDC) has estimated that by 2021, global investments in Artificial Intelligence methods (including Deep Learning and Machine Learning) will increase by 57 billion USD from current levels. According to reports from KPMG, investments in AI should increase at a compound annual growth rate (CAGR) of 46 percent during 2016-21. It is expected that financial services will contribute around 18 percent to overall AI expenses.

How Big Data, AI and ML can help improve operational efficiency? 

When fintech companies encounter challenges to improving their operational efficiency they can turn to Big Data Analytics, AI and ML. It is imperative that fintech companies incorporate these technologies into their business processes and the algorithms being used for completion of business tasks. 

Deep Learning tools can be one of the biggest assets when it comes to developing the most useful practices in business process optimisation. AI can lead to the creation of high-quality automated solutionsHence, this area of operational efficiency can be elevated to expected levels in quick time and with relatively low investment.  

The risk management solutions used by all fintech companies need to be powered by Big Data Analytics, AI and ML. Predictive Analytics tools using Big Data can analyse large amounts of data to find patterns and detect instances of fraud and money laundering.  

Real-time analytics applications can ensure all of this is done in a matter of minutes. As a result, fintech companies can take effective measures as soon as possible. The use of these tools in risk management solutions will always lead to compliance with even the strictest regulatory requirements. So, the overall operational efficiency in risk management can be enhanced. 

Big Data Analytics are ideal for customer sentiment analysis. They can provide fintech companies with valuable insights about the merits and demerits of their existing operations and methods of service delivery. This information allows fintech companies to make their services seamless, user-friendly and up to speed with the demands of existing and potential customers.  

Wrapping up

It is safe to conclude that all aspects of operational efficiency enhancement can be improved with the use of Big Data Analytics, AI and ML. This leads to holistic improvements at the organisational level.  

We believe in offering solutions on a par with the highest global standards, allowing our clients to improve their operational efficiency in all areas.

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