Technology and data has been a fixture of the financial industry for decades now and is becoming increasingly important.
As more and more data is being collected, data science techniques will help financial industries to reduce operational costs, enhance security and fraud detection, and increase revenues.
Financial institutions, including banks and credit card companies, insurance companies, and advisory services, often have more data on their customers than anyone else. Rich real-time data for every product that financial institutions sell, every process that they use to deliver those products, and almost every action that customers make on their app or website is recorded. In addition to third party data, this mountain of data can be used to reshape business strategies and make more informed decisions. Conlan Scientific can work with clients to address these sorts of problems in financial services:
Pricing optimization allows companies to understand how customers will respond to different prices for its products or services. Companies that use pricing optimization technology can gain valuable insights into how customer preferences impact profitability, while meeting their company objectives and becoming more customer-centric in their decision-making.
Pricing optimization can be difficult to execute because even customers with identical demographic characteristics (e.g., income, location, age, gender) may have different degrees of price sensitivity. For example, in the case of financial services, customers with low price sensitivity may place greater value on other features of a transaction (e.g., convenience of brand values, service levels, features of the product). By collecting data on these additional features and identifying customer preferences, pricing optimization can approximate customers’ reactions to a change in price and derives an expression for the optimal price at which a product should be sold to maximize expected returns (Phillips, 2005). By crafting optimal pricing strategies, companies can acquire and retain more profitable customers and ultimately maximize returns.
Customers have distinct preferences, and businesses can learn who their customers are by collecting data through various channels: surveys, the success or failure of sales calls, click-through rates, social media analysis, content marketing, etc. Creating customer profiles and segmenting your customer base can be one way to understand characteristics of your customers and what they are likely to buy or invest in.
For example, a business may be interested in what features or characteristics of their customers correlate with the likelihood of purchasing a new product or using a new service. Past behaviors, transaction history, social media use, location, income, education are just a few of the pieces of data that can be collected for analysis. Some of these features may correlate with the likelihood of purchasing a new service or product (e.g., using a robo-advisor). Data might suggest that Millenials and Gen X’ers are more likely to use robo-advisors, whereas Baby Boomers prefer speaking to a financial advisor directly. Product development relies on behavioral and market analysis to assess whether it’s advantageous to create a new offering. And even after it’s launched, the company needs to figure out how to best advertise and market the product. All of this requires data science including prediction and inferencing.
Process automation is one of the most common applications of data science and machine learning in the financial services industry. Examples include:
It’s critical to first establish what aspects of the business operations can or should be automated. For one, the process should be rule-based and depend very little on decision-making; it should be scalable and fit within the organization’s current processes; and it should rely on large amounts of recorded data. Process automation helps to take repetitive, rule-based tasks and make them streamlined processes that improves productivity and boosts accuracy.
Risk analysis, to some extent, occurs throughout all industries. Even at the individual level, we unconsciously or consciously weight the risk of (future) events and engage in risk evaluation. Bigger risks, e.g., loaning someone your favorite car or lending them millions of dollars, require more data to to make an informed decision.
Likewise, financial markets need to compute different risk levels at any given time, and the input values are also varied: the weather (especially for commodities), competitors, political decisions, customer preferences, personal and business credit scores (creditworthiness), and so on. With so many different factors, automating risk analysis is an area where machine learning and data science can have a large impact.
At this point, algorithms can quickly provide a prediction for whether or not a customer is likely to repay a home loan, private loan, or credit card balance. Algorithms such as neural networks, random forests, or gradient boosting, are capable of producing recommendations at each stage of the risk analysis process. Preprocessing of the data, feature selection, risk classification, and mapping if-then scenarios to risk patterns are becoming more reliant on machine learning and AI algorithms.
As the number of transactions, third-party integrations, and users continue to grow, security threats in finance are increasing. And machine learning algorithms are becoming more accurate at detecting fraudulent behaviors.
For instance, customers’ location, amounts, financial transactions, time of transactions, merchant/vendors that they pay are recorded in real-time. Machine learning algorithms can analyze each action a customer takes and assess if the behavior is characteristic of previous behavior. Automatically, the system can request additional identification from the user to validate the transaction or block it if there is at least 95% probability of it being fraudulent. The speed at which the system processes data and makes a prediction helps to prevent frauds in real time, not after the crime has already been committed.
Phillips, R. L. (2005). Pricing and revenue optimization. Stanford University Press.