At its most fundamental level, data science applies algorithms to data for the purpose of gaining insights from that data. Data science integration, then, is just the process of incorporating data science into business processes. But the breakthrough isn't just about the discovery of new ways to quickly analyze massive volumes of data — that's only the beginning. The real revolution in data science in finance lies in innovations that leverage niche-specific and real-time analysis across the financial sector.

In risk analytics, data scientists gain insights from data analysis and apply those insights to make risk-reducing operational and infrastructural changes to a company's operations. Risk analytics can address everything from assessing loan customers for probability of default to analyzing masses of individual bank transactions for patterns that indicate suspicious activity.

These insights can help companies prevent fraud, reduce losses, and even identify high-risk investments or transactions before they cost an institution time and money.

Risk analytics is a field designed to prevent financial mistakes (or disasters) before they happen; fraud detection is the practice of figuring out if a mishap has already occurred.

As a core component of any financial business, fraud detection is the identification of fraudulent transactions, scams, and unauthorized account access. Typically these infractions involve a "bad actor" intentionally acquiring financial assets under misleading or outright false pretenses — although increasingly fraud can come in the form of cybersecurity attacks and data breaches that don't exhibit any fraudulent traits.

Fraud detection work has advanced significantly since data scientists have begun to teach machine learning systems right from wrong by feeding them volumes of structured data in the form of fraudulent and legitimate transactions. These datasets inform the patterns that machine learning and AI solutions look for in security operations (much as they do when systems conduct real-time analytics in areas such as market surveillance and customer behavior). ML fraud detection not only monitors incoming data every second of the day, but also detects patterns that human investigators could never catch.

Customer data management has evolved by an order of magnitude since big data's big breakthrough in the mid-2000s. Financial institutions have always received large amounts of customer information in the process of their operations, but in the past that data was typically used only for financial analysis. But consumer data — from transaction history to personal information and even tertiary information like individuals' social media presence — can play a powerful part in enhancing marketing reach and customer satisfaction.

Functions that have emerged from applications of data science include the personalization of product advertisements, automating discount offerings for qualifying customers (you know those "If you spend $10 more, your order will be eligible for free shipping!"), and tailoring product offerings to specific groups or individuals (Think "Happy birthday! Here's 10% off our latest product" emails that start rolling in leading up to your birthday). Consumer data analysis also allows companies to construct consumer identities, which allows for stronger targeting of potential customers.

The more a business knows about its customers, the better it can serve them. Algorithms, machine learning, and AI solutions are all helpful in that cause. Data engineers working in personalized services gain an understanding of customers through segmentation, which is the process of identifying customers by data points that indicate how likely a customer is to benefit from a given financial service or product.

As the financial landscape evolves along with the complexity of financial products, customer segmentation is more than a good idea; it's a competitive necessity. Personalized services have become an expectation by customers: In a report conducted by Salesforce, 66 percent of customers expect businesses to understand their needs.

Data science has fundamentally changed the game for securities trading, full stop. The driver of this change is a form of automated software called algorithmic trading, which makes data-backed, analytics-driven trades at lightning speed. (It's estimated that 63-70 percent of all trades in stock markets around the world are now done through algorithmic trading.)

The simplest forms of algorithmic trading consist of systems that initiate trades based on rudimentary sets of parameters (e.g., "When Stock X hits $5 per share, buy 100 shares."). But with data scientists applying big data insights, trading becomes even more sophisticated. Feed a machine learning system large-enough volumes of market data to analyze, and before long an AI can make predictions. A trading algorithm can then use these predictions to build more reliable, nuanced, and profitable parameters to use in making real-time trading decisions.

Before the incorporation of data science into financial operations, technological limitations meant that financial data had to be divided into analyzable categories before it could be processed. That created bottlenecks between receiving data and generating actionable analysis. Data science engineers remove those bottlenecks by building AI-driven frameworks capable of processing raw, uncategorized data as it comes in.

Gathering and analyzing data simultaneously delivers real-world benefits. For example, fraud in some cases can be detected even as it's being committed, rather than days, weeks or even months after fraudulent transactions have cleared; the data generated by the different branches of a financial institution can be analyzed as the data is produced, giving companies real-time information to guide decision-making.

Data science is helping financial companies of every size and stripe find insights in all corners of their operations — and to turn those insights into next-level profits.