Analysts, traders, and computer scientists have been looking at more advanced ways to make and save money for decades. The industry itself is prime for machine learning, as it has immense historical records, large data sets, and a quantifiable method of measuring results. For this article, we will be focusing on fraud detection, sentiment analysis, predictive analytics, underwriting, customer service, financial product recommendations, and image recognition in claims.
The most obvious usage of machine learning involves fraud detection. This is nothing new, and it has been used for a few years by companies like PayPal. Every data set is different and requires its own training and fine-tuning. Suspicious behavior should be reported to a consultant using augmented intelligence (AI) to complement human cognitive skills of recognizing fraud patterns. Good UX drastically improves the performance of each consultant and their accuracy. Good-looking UI accelerates the adoption of the tool. We have experience in crafting fraud detection tools, engineering leaner manual review processes, and building a dashboard to analyze the performance of each consultant.
Communication with financial institutions is shifting toward messaging and phone/video calls. Thousands of interactions with call centers are impossible to monitor and evaluate with traditional methods. This is where natural language processing (NLP), in combination with sentiment analysis, can be immensely useful. Sentiment analysis can improve manual reviewing of phone calls, optimize agent delegation by individual skills, and prioritize interaction with panicked or stressed customers. The same methods can be used while in a chat or on a call with a debtor to assess their honesty. Furthermore, we could automatically assess the state of a company’s public perception with an aggregated Twitter sentiment analysis or a historical news sentiment analysis.
What better way to increase our earnings than by predicting the future. Predictions are never guaranteed, they only have a confidence score. Behavioral patterns, be they macro or micro, are the most significant features we should look for when doing a historical analysis or trying to use predictive analytics. Overfitting trading models can be a concern because it is difficult to account for market volatility. Once we get outside of trading, we can find room for all sorts of uses. In subprime lending, we can predict which of our users’ credit behavior will improve over time. A vehicle loan that seems too risky right now may become acceptable when we consider the benefits provided to the individual when they have access to a personal vehicle. Venture capitalists could look at characteristics and behaviors of founders in relation to the financial situation of startups. By combining new datasets, trends, technological development, sentiment analysis, and predictive analytics, we could even adjust actuarial models.
Conventional methods of underwriting are not poor, but like any model, could always be better. Securities, banking, and insurance fields can all benefit from advances in technology. Data collection and client histories provide us with a significant corpus of data to train our models with. Long histories are significantly more useful, as they can limit potential overfitting and idiosyncrasies arising from market conditions during certain years. Machine learning models decrease the risk associated with current borrows while increasing the number of approvals. Behavioral trends, for example, can indicate added risks associated with the driving habits of certain vehicle owners. Such predictive models can assist in serving areas that have been traditionally underserved. These customer segments do not have a long financial history, but insights from similar clients can be transferred to more accurately infer the risk involved. Our reproducible models alleviate regulatory issues, as it becomes easier to audit the decision process involved.
Customer Service is one of the most resource-consuming processes within the finance and insurance industries. Recent advances in NLP have increased the level of automation in document processing and speed of resolving customer issues. As chat is growing and becoming one of the most prevalent forms of communication between consumers and businesses, augmented intelligence solutions allow agents to reply with recommended predetermined answers that can be highly personalized. Text sentiment can be detected, allowing an agent to prepare for conflict resolution and receive additional recommendations on how to lower tension. On the other hand, machine learning can help to pre-assess insurance claims or credit requests.
When a product recommendation does not address the actual needs of a customer, it has a negative impact on the business’s relationship with that customer. Personalized product recommendations based on customer data can be applied to a few different aspects of finance. Consumer applications start with analyzing a given customer’s behaviors, habits, and other associated data points. With these analytics in hand, we can disseminate the types of products our users will find most useful. We can tell which users will be the most inclined to take advantage of a new credit card offer or consider an investment opportunity, instead of relying on cold calls containing random offers. The business side of such recommendations works on a much larger scale. Here, we must take into consideration the entire swath of data. Marketing and product teams can then take advantage of this data to create new promotions or packages based on unfilled market opportunities.
Mobile applications have allowed users to take pictures of their assets when filing an insurance claim, thereby increasing their own convenience while lowering costs associated with estimators. But, an agent was still required to assess these images. That agent would then flag claims that require a human to review the case in person. Recent advances in deep learning have increased image recognition accuracy to levels that surpass humans. With that in mind, we can automatically review claims submitted and flag cases that need manual review almost instantaneously. Customers would have their claims paid out in minutes, dramatically increasing satisfaction. Image recognition can also be used in fraud detection by verifying signatures between documents or data consistency in scanned contracts, as there is always a risk that a customer will swap contracts before signing.