The State of Financial Analytics 2019


The global economy moves at an impressive pace. Then there is the global financial economy, which is in a league all its own.

The advent of technology has challenged a lot of industries to evolve, but the financial services sector is on the shortlist of benefactors.

To gather data, analyze it, and extract value on a constant influx of raw information requires scalable software, fast data mining and intelligent insight generation. The current state of financial analytics reflects these things and more. Let’s dive in below.

Financial Data Is Growing Exponentially

All data is increasing at exorbitant rates, but financial data generates a significant amount of it. Billions upon billions of transactions take place each day through a variety of products, apps, and integrations. Data is created from each of these financial interactions. Financial firms are thrilled, except for the fact that data volumes are also proliferating at rapid rates. More data volumes are making cloud business intelligence and analytics platforms non-negotiable for companies. On-premise and otherwise limited analytics systems are simply no match for the number of sources today’s data derives from.

Eve of Massive Industry Growth

We’ve seen surveys and reports the last few years that portrayed analytics as important to companies but not as commonly practiced. The question of why more companies don’t prioritize and integrate data into their daily workflows is still a mystery.

However, perhaps we should expect different results on those surveys going forward. This year marks the start of what’s expected to be a decade of significant growth in the global financial analytics market. Per Cision, the industry is forecasted to grow at a 10 percent compound annual growth rate (CAGR) between 2019–2027.

Companies are finally aligning their ideals with primary business objectives. They’re also trying to meet employee expectations. Features like self-service access and personalization are key aspects of financial analytics tools that attempt to mirror everyday workflows with the conveniences in employees’ personal lives.

Machine Learning as a Service (MLaaS)

Personalization, ease of use, scalability, and speed are all integral to modern analytics offerings, but the most promising—and invested—area of all is experimenting with machine learning. Machine learning solutions have gotten so popular that an acronym—MLaaS—now sums it up. And with good reason, the industry is expected to hit nearly $20 billion by 2025, up from just over $1 billion in 2016, per Transparency Market Research.

The report states that healthcare and life sciences will be the driving factors of this growth, but financial services, banking, insurance, and other fiscal-focused verticals won’t be far behind.Firms will either invest significantly in their own machine learning analytics infrastructure or leverage third-party ML software and tools to access the rich insights in their data.

Engaging Insights and Analyst Safeguards

The financial world moves too quickly for regular reporting intervals. This increases the need for ad-hoc knowledge, and to more than just the data-inclined. But when a firm changes their data habits like this, they need to ensure that end users are interpreting information correctly.

Incorporating visualizations isn’t exactly new among business intelligence and analytics technologies. However, these views are usually limited and aren’t created to be aesthetically pleasing. Modern financial analytics tools are changing how data is digested. Users can sort insights among dozens of different visualization types. More importantly, they’re alerted of findings they may have missed, things like anomalies, indicators, and trends.

Software with this kind of methodology gives financial companies the confidence to broaden their data access without worrying about end users interacting with data effectively on their own.

Booming industry growth, the advent of machine learning as a service, and AI-powered insights are fueling the analytics space to meet the unique needs of businesses. Other driving components include the increasing use of dark data, predictive analytics, the rise of the chief data officer, and the continued proliferation of the Internet of Things (IoT) devices and data. Some of these developments are less clear than others, but watching the industry unfold going forward should be exciting regardless.

Anik is an IT professional and Data Science Enthusiast. He loves to spend a lot of time testing and reviewing the latest gadgets and software. He likes all things tech and his passion for smartphones is only matched by his passion for Sci-Fi TV Series.