Everything you need to know about financial data analysis

With 46% of customers using only digital banking channels, fintech is gradually taking over the world. The value of the global fintech market was $110.57 billion in 2020, and by 2030, it is anticipated to reach $698.48 billion.

According to the KPMG Australian Fintech Survey Report 2022, Australia’s fintech industry is rapidly expanding in terms of the number of new businesses being founded, the amount and value of investments being made, and the interest and demand for tech-integrated solutions. The traditional finance industry is forced to prioritise digital innovation at the top of its strategic priorities. The number of Australian fintech businesses is expected to double between 2017 and 2021.

Data analytics is one of the most significant factors influencing the promising future of fintech. It’s what enables fintech businesses to decide quickly and accurately so they can better serve their clients. Financial Planning Software and data analytics go hand in hand.

Data in conventional financial institutions is frequently compartmentalised into various departments and is not always accessible or utilised efficiently. The use of data analytics by fintech companies to advance their business models has advanced significantly. Fintech companies now have the computing power to provide more comprehensive services from both an internal and external standpoint.

 

 

Four main types of data analytics

 

 

Predictive data analytics

 

The most popular subset of data analytics is predictive analytics. Predictive analytics is used by businesses to find trends, correlations, and causes. Although the category can be further divided into predictive modelling and statistical modelling, it’s crucial to understand that the two are interrelated.

 

Prescriptive data analytics

 

Prescriptive analytics is where AI and big data are combined to help predict outcomes and determine the best course of action. The two subcategories of this analytics category are optimisation and random testing. Prescriptive analytics, with the use of ML developments, can assist in providing answers to queries like “What if we try this?” and “What is the best action. You can evaluate the right variables and even recommend brand-new ones that have a better chance of producing a successful result.

 

Diagnostic data analytics

 

Using data from the past to guide your business can be greatly beneficial. Analysing data to understand causes and events or why something happened is known as diagnostic data analytics. Techniques that are most often used are drill down, data discovery, data mining, and correlation.

Diagnostic data analytics provide an explanation why something happened. It is divided into two additional categories, discover and alerts and query and drill downs. To extract more information from a report, query and drill downs are used.

 

Descriptive data analytics

 

The foundation of reporting is descriptive analytics, without which business intelligence (BI) tools and dashboards are inconceivable. It responds to fundamental inquiries like “how many, when, where, and what.”

Ad hoc reporting and canned reports are the two additional categories into which descriptive analytics can be divided. A canned report is one that has already been created and contains details about a particular subject. On the other hand, ad hoc reports are usually not scheduled and are created by you. They are produced when a specific business question needs to be addressed. These reports are helpful for learning more specific details about a query.

 

 

Data Analytics is key to the continued evolution of Fintech

 

Unlike any other industry, fintech has seen a boom in recent years. Major investments have flooded the market because of field accelerations. According to CBI Insights, there are 1,021 unicorn companies (a private company valued at over $1 billion) in the world with a cumulative valuation of $3.3 trillion.

The benefits of fintech extend beyond the players in the industry; regular consumers also gain from it. Consumers can now access solutions in real-time thanks to financial services and other business-related concerns. Fintech continues to redefine itself, just like any other rapidly expanding industry, with game-changing innovations appearing several times a year.

As a result, the level of competition in the industry has risen, forcing established key players to adopt innovations they hadn’t previously considered. PWC reports that 77% of financial institutions are expected to increase their focus on innovation in the interest of consumer retention.

Considering this, one might wonder what the future of fintech holds. How can some businesses use analytics to stay competitive in the expanding market?

In fintech, data analytics lays the groundwork for a company’s understanding of its customers. Analytics provides information on the needs, wants, and expectations of customers. A fintech company, or any business, can better serve its customers with personalised and relevant solutions, the better it understands them. Modern businesses rely on analytics, making it essential for enhancing both growth and customer service.

With every passing day, our world’s reliance on data is increasing. Businesses in the fintech industry need to know how to collect, track, organise, scale, and use analytics to ensure success and growth.

 

 

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