From Bookkeeping to Predictive AI: The Evolution of Data Analytics in Financial Services

In today’s era of algorithms, it is difficult to talk about financial services without terms such as ‘data analytics’, ‘machine learning’, and ‘AI’ making their way into the conversation. That said, you would be right in assuming that the financial services industry must always have been data-driven. 

From the early days of double-entry bookkeeping to today's sophisticated algorithms that power high-frequency trading, data has been the cornerstone of financial decision making. 

The inception of financial data analytics began with simple statistical methods and evolved into advanced predictive models and machine learning algorithms. The coming of the digital age only amplified the volume and diversity of data handling, as well as enhanced the speed and precision of financial decision making.

This evolution of data analytics in financial services is marked by several key phases. Initially, data analytics focused on historical data analysis for reporting and compliance. With the advent of computers, its scope expanded to include real-time data processing and advanced analytics for risk management and fraud detection. 

Most recently, the integration of big data and AI has ushered in a new era, enabling financial institutions to harness massive datasets and derive actionable insights that drive strategic decision making and operational efficiency​. 

Applications of Data Analytics in Financial Services

Data analytics including AI in financial modeling is used in various ways across the finance sector, transforming how institutions operate and compete. Let’s look into a few of them:

• Risk management is one key application. By examining extensive data sets, financial institutions can detect trends and forecast potential risks more accurately, helping with credit risk evaluation, market risk assessment, and operational risk management.
• Fraud detection and prevention is another critical area where financial data analytics plays a pivotal role. Advanced algorithms and machine learning models analyze transaction data in real time to detect unusual patterns and flag potentially fraudulent activities. This not only helps prevent fraud but also improves customer trust and satisfaction​.
• Customer analytics is also a significant application where financial institutions use data to understand customer behavior, preferences, and needs. This information is crucial for developing personalized financial products and services, enhancing customer experiences, and increasing retention rates.
• Regulatory compliance can be simplified with data analytics. As per Deloitte, the Australian Securities and Investments Commission (ASIC) is exploring ways to detect risk in mandatory recurring data collection, particularly regarding managed funds flow and mortgages. Similarly, financial service providers must leverage organizational intelligence to identify failures in meeting compliance so that they can be avoided. They must also meet the intent of regulatory bodies to ensure positive (and legally bulletproof) customer experiences. 

Let’s explore a couple of real-world examples as illustrations of the impact that data analytics can have. 

In 2017, JPMorgan Chase introduced COIN (Contract Intelligence), a software that uses AI and machine learning to automate the review of commercial loan agreements. What once required 360,000 hours of effort every year from the lawyers and loan officers could now be achieved in mere seconds. 

By using data to identify relationships and patterns, not only did the initiative significantly reduce the time for document review, increase accuracy, and free up the legal staff’s time for more complicated tasks, it also allowed the bank to reduce human errors related to loan servicing, amounting to about 12,000 new contracts every year. 

In another example, PayPal leveraged data analytics to develop sophisticated fraud detection systems that analyze millions of transactions per second, helping protect its customers and reducing losses. 

Now that you are aware of the kind of impact data analytics in financial services can have, let us understand the potential pitfalls financial leaders may face along the way. 

Challenges of Data Analytics in Financial Services

Despite the numerous benefits, there are several challenges associated with data analytics in finance:

1. Data privacy and security:

Financial institutions handle sensitive data that must be protected from breaches and unauthorized access. Ensuring data privacy and security while leveraging analytics is a significant challenge.

2. Regulatory compliance:

The regulatory landscape is continually evolving, and financial institutions must ensure that their data analytics practices comply with all applicable laws and regulations. This requires constant monitoring and adaptation of analytics processes​.

3. Data quality and integration:

The efficacy of data analytics relies heavily on the quality as well as the comprehensiveness of the data. Financial institutions often struggle with integrating data from disparate sources and ensuring its accuracy and reliability​.

4. Talent shortage:

There is a growing demand for data scientists and analytics professionals in the finance sector. Finding and retaining skilled talent is a challenge that many financial institutions face​​.

Trends in Data Analytics for Financial Services

The future of data analytics in financial services is being shaped by several emerging trends:

1. AI and machine learning:

The adoption of AI and machine learning is set to grow, providing financial institutions with powerful tools to analyze data, predict trends, and automate processes​.

2. Real-time analytics:

The ability to process and analyze data in real time is becoming increasingly important. Real-time analytics enables financial institutions to make faster and better-informed decisions​​.

3. Big data integration:

‍As the volume of data generated continues to grow, integrating big data with existing analytics systems will become essential. This integration will provide deeper insights and enhance decision-making capabilities​​.

4. Blockchain and data security:

Blockchain technology offers a secure and transparent way to manage financial transactions and data. Its adoption in data analytics can enhance security and trust in financial services​.

5. Customer-centric analytics:

The focus on understanding and serving customers better will drive the development of more advanced customer analytics tools. These tools will enable financial institutions to offer highly personalized services and improve customer experiences​​.

6. Generative AI:

Generative AI can automate content creation, generate insights, and simulate financial scenarios, significantly enhancing analytical capabilities. McKinsey estimates that this technology could add $200 billion to $340 billion annually to the banking sector by increasing productivity and creating new business opportunities.

Vital Financial Data Analytics Software and Capabilities 

Several tools and technologies are essential for effective financial services data analytics:

1. Hadoop and big data platforms:

These platforms allow financial institutions to store, process, and analyze vast amounts of data efficiently. They are crucial for handling big data analytics tasks​​.

2. Machine learning libraries:

Tools like TensorFlow, Scikit-learn, and PyTorch provide the frameworks needed to develop and deploy machine learning models for various financial applications​.‍

3. Data visualization tools:

Tools like Tableau, Power BI, and QlikView help financial institutions visualize complex data sets, making it easier to identify trends and patterns​​.

4. Predictive analytics software:

Financial data analytics software, including ones from SAS, IBM SPSS, and RapidMiner, enable financial institutions to build predictive models that forecast future trends and behaviors​​.

5. Natural Language Processing (NLP):

NLP tools help financial institutions analyze unstructured data, such as customer feedback and social media posts, to gain insight into customer sentiment and preferences​.

Savant Labs: Revolutionizing Data Analytics in Financial Services

Data analytics is vital for financial organizations to survive (and thrive) in the future. That said, people who deal with oceans of data often end up spending too much time wrestling with the data (repeatedly so) to distill meaning from it, or they work in silos, which defeats the whole purpose of deriving useful intelligence from data analytics. This is where Savant steps in. 

Founder and CEO of Savant Labs, Chitrang Shah, pointed out in an interview with TechCrunch, that “the amount of manual work that goes on in doing analytics and reporting is staggering.” 

He also noted that the data market is highly fragmented, and most analytics solutions are primarily built for either central analytics or data engineering teams. “Nobody is actually solving the problem for people that are sitting in the business functions.”

That is why the driving force behind the creation of Savant’s platform was a vision to revolutionize how financial institutions handle and capitalize on their data, enhancing efficiency and generating actionable insights. The solution stands out by providing effortless analytics automation. 

For example, its cloud-native platform allows instant data access from over 200 sources, enabling financial institutions to create analytics workflows in minutes using a drag-and-drop interface. 

Impact of Savant’s Automation of Data Analytics in Financial Services

Savant’s cloud-native, generative-AI-powered analytics automation platform accelerates insights from diverse data sources and SaaS applications. 

The platform achieves business outcomes five to ten times faster due to key features like:

• Intuitive workflows: The platform offers easy visualization of workflows that combine analytics flows, data flows, and uniquely combined bots. These bots automate the delivery of insights to over 100 destination platforms, dashboards, and applications.
• Pre-built templates: Savant Labs provides pre-built analytics automation templates that simplify the creation of tailored workflows.
• Instant data access: The platform allows instant data access from over 200 sources, enabling financial institutions to create AI-integrated analytics workflows in minutes.

One area where Savant particularly shines for finance and accounting professionals is simplifying cash flow forecasting. By automating intricate data processes to offer transparent and tailored calculations, financial providers can focus on forecasting strategies and outcomes rather than data manipulation.

The platform includes a comprehensive suite of features for creating cash flow statements, chart analyses, trend analyses, and more. This enables businesses to test the accuracy of their analyses with a predictive mindset and implement fail-safes for adverse scenarios.

In a highly volatile world where the cost of capital is increasing, the ability to forecast cash flows in minutes strengthens businesses’ financial health, ensuring stability and resilience.

Adopting a Predictive Mindset With Data Analytics in Financial Services

The financial services industry stands at the brink of a transformative era, driven by the relentless advancement of data analytics. Institutions that are effectively able to harness their data’s full potential will not only survive but thrive in this competitive landscape. 

The ability to analyze vast amounts of financial data in real time, predict trends, and automate complex processes is no longer a luxury but a necessity. This evolution demands a strategic approach that combines innovative technology with a deep understanding of the financial ecosystem.

Savant recognizes that one size doesn’t fit all, and thus offers seamless integrations with data sources and business intelligence platforms to build custom workflows tailored to the unique needs of each financial institution. 

Learn how businesses are successfully embracing the future of data analytics in financial services with Savant’s analytics automation, and transforming their data into a strategic asset. 

Discover more about the Savant platform here.