Over the years, the applications of data science methods are producing limitless innovations for various industries. For the finance world this is nothing new. The usage of quants and algorithmic trading has been goin on for decades. However, now with increased computation and cheap storage the ability to automate and improve these models have drastically changed.
The finance industry has been benefiting from the integrating data science and statistics into their processes for decades.
Several use cases are presently helpful for enhancing the customer experience, improving models and helping fight the data imbalance problem. These use cases are set to shape the future, and give rise to new processes in time to come.
The success witnessed in several use cases comes from the ability to seek the right algorithms, assembling suitable datasets, and building well-organized infrastructure. Data science is creating significant inroads within the financial services industry. And the solutions offered are seeing more implementation with Artificial Intelligence, Machine Learning, and Reinforcement Learning.
With the rapid increase in computing power and ability to store larger amounts of data, financial companies have a lot of opportunities to both improve the customer experience as well as increase profits. Here are a few great examples of how companies are using data science in the finance industry today.
1. Process Automation — Improving Scale Of Information (Robo Advisors And Customer Chatbots)
Process automation might not seem like a data science topic.
However, for almost every predictive model, there is an automated process required to ensure that the data being ingested is constantly up to date.
The new technologies developed are seeking to automate repetitive processes, change manual tasks, and enhance productivity. Consequently, data science is also enabling several brands to create better scale-up solutions, cost optimization processes, and customer experience. Below are some of the major process automation use cases of data science process in the finance world:
A few great examples of large corporations automating processes include JPMorgan Chase which unveiled its Contract Intelligence (COiN) platform. This platform makes use of natural language processing(NLP).
The platform offers a solution to processing legal documents and extraction of vital data from them. A typical manual review would approximately take 360,000 labor hours for 12,000 annual commercial credit agreements. But, with the new Contract Intelligence (COiN) platform, the time to review a similar number of contracts would be only a few hours.
BNY Mello is also integrating process automation into its banking network. This system is responsible for several operational improvements and also responsible for $300,000 in annual savings.
Another example is Wells Fargo. It is using an AI-driven chatbot. The chatbot is set to communicate with users via the Facebook Messenger platform and offer assistance as regards to accounts and passwords.
All of these systems will inevitably have some form of models that are amplified by process automation.
2. Improving Back And Forward Testing
In a live market, traders with a keen interest in trying new trading ideas often will develop models by backtesting outcomes to dictate if a system would be profitable. Backtesting denotes the application of a trading process to past data in verifying how it would perform during a specific time frame. Nowadays, backtesting in trading platforms offers:
Backtesting remains a valuable option available in many third-party tools. The division of data into multiple sets (out-of-sample and in-sample testing) can allow traders with a practical and useful approach to evaluate a trading idea and system.
As a result, most traders apply optimization techniques in backtesting in evaluating systems and integrate variables from user-defined input that permit system “tweaks.”
While backtesting offers valuable information, it may be misleading as it is just a part of the process of evaluation.
Forward performance and out-of-sample testing offer more confirmation as regards to the effectiveness of a system and can give a real response to a system’s performance before the involvement of real money.
Forward performance offers traders a new range of out-of-sample information for system evaluation. It simulates actual trading and entails how such systems would act in an active marketplace.
A key aspect to forward performance testing includes the exact following for the system’s logic, which helps to precisely evaluate the process.
A good correlation amongst backtesting, forward performance testing, and out-of-sample testing results is key to defining the practicability of a trading system. The continual use of forward performance testing and out-of-sample testing offers an extra safety layer before placing the system for market use. Positive outcomes alongside good correlation amongst out-of-sample and in-sample backtesting with forward performance testing enhance the prospect of a system performing well in actual trading.
3. Fighting The Imbalance Problem For Fraud Detection
The detection of financial fraud using data science is another major use case that is both currently being used and constantly improved.
Even with new methods of security such as adding a chip into cards and current predictive models that are attempting to reduce the fraudulent claims are far from perfect.
It is still predicted that online card fraud will rise to a tune of $32 billion in 2020.
With data science, issues like fraud detection are typically considered as classification problems, which is the prediction of the class label with discrete output given a data observation.
To solve such problems with data science, it requires the creation of models with enough intelligence to accurately classify transactions as either fraudulent or legit based on transaction details.
This unique solution using the data science approach also faces a significant challenge termed imbalanced data.
In real-world finance, it usually arises from having data with a large part of its transaction classified as legit while only a small portion of the transaction are fraudulent. This is a similar problem with most fraud including insurance and healthcare.
For imbalanced data, the bottom-line is the prediction might be 99% accurate but the 1% of inaccuracy would be the fraud claims being labeled non-fraud.
Investment in technology for tackling fraud has been on the rise over the years. Nevertheless, since imbalanced data offers a unique instance with most variables adding no context, it is always starting with some EDA (Exploratory Data Analysis) before using any prediction models. This allows the data scientists to see trends in the fraudulent claims themselves. Often times you can take a large data set of valid and invalid transactions and find traits that are unique to a subset of said transactions.
From there you can build a model on that subset of data to provide improved balance.
In addition, after EDA, there are several approaches to dealing with imbalanced data. The three popular approaches that stand out include combined class methods, oversampling, and undersampling.
The benefits of data science in the finance world are seeking an enhanced customer experience, as well as applications for the future to offer more optimized systems and processes. By 2020,
Gartner forecasts a massive 84% of customer dealings with any enterprise would happen without another human interaction. With data science set to drive this revolution, the finance world can leverage more its applications to deliver practical solutions to proper use of data, security threats, and more. The future of the finance world is set to witness even more significant growth.
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