Over the last decade, the use of AI has exploded. Machine learning and deep learning algorithms and models process an immense amount of data to enable faster, smarter, and better business decisions. As such, machine learning forecasting for the financial industry holds incredible potential for banks, the historical custodians of vast stores of data. However, the technology’s direct impact is still marginal as only a few institutions have capitalized on the technology’s extensive potential.
BCG estimates that businesses and banks that embrace ambitious AI strategies can add 15-20% to their bottom line in one to two years. McKinsey expects that machine learning technologies in banking could potentially deliver up to $1 trillion of additional value for the global banking industry each year.
To help business leaders capture the value machine learning in banking technologies holds for them, we propose answers to the following questions:
- What is the difference between machine learning and deep learning?
- How are machine learning and deep learning used for forecasting and prediction?
- How do banks use machine learning and deep learning for forecasting and prediction?
What is the difference between machine learning and deep learning?
Artificial intelligence, machine learning, neural networks, and deep learning are often misleadingly used interchangeably in media, creating ambiguity about them. When, in fact, they are essentially subsets or progressions of each prior term. As such, deep learning is a subset of machine learning, and both are subfields of artificial intelligence.
Source: NVIDIA
Although deep learning and machine learning function in a similar fashion, their capabilities are different. Machine learning models can become progressively better with iterations, but humans must correct inaccurate predictions generated by the algorithms. On the other hand, a deep learning model can determine whether its predictions are accurate or not on its own.
For example, a machine learning algorithm can be taught to spot a suspicious transaction and flag it as fraudulent by feeding it a usually structured dataset to learn from. It depends on human intervention to determine the differences between data inputs and their characteristics, such as time, date, amount, and location of a transaction. As it continues to learn, it will flag any transaction when it spots certain suspicious behaviors it recognizes.
Now, a deep learning model automates much of the fraudulent behavior extraction process, eliminating some of the required human input. As deep learning models have multiple neural networks, large unstructured datasets can be processed more efficiently and faster than with a machine learning algorithm. This is an important point because unstructured data comprises 80-90% of data found in companies worldwide, according to IBM.
Besides preventing fraud, machine learning and deep learning enable banks to improve user experience, optimize services, automate processes, and predict customer churn. Leading financial institutions are already employing data science tools that help them drive sales and revenue.
Source: KPMG
How are machine learning and deep learning used for forecasting and prediction in banking?
Using machine learning in banking and deep learning for banking forecasting and predictions are powerful tools that help to make better-informed business decisions. As the most recent pandemic demonstrated, consumer behavior can change drastically over the course of just a few months. With stores closed under lockdown, consumer purchasing surged online. Datasets used to train static banking anomaly detection systems did not have any remotely similar patterns. As a result, many financial institutions worldwide saw their anomaly detection anti-fraud systems fail.
Adopting machine learning in banking circumvents the deficiencies of static systems by continuously learning from fresh incoming data. Forecasting for banking using deep learning can generate even better results with less human input. Both technologies can generate valuable insights from vast amounts of data that can be used to forecast and predict consumer and business behavior.
It is important to note that forecasting and prediction mean different things, much for the same reason weather forecasting is not called weather prediction. A prediction model uses a training dataset to estimate the outcomes for new data points. On the other hand, forecasting adds a temporal dimension into the equation to make estimates based on time-series data. Forecasting models depend on previous, most recent observations to make future estimates instead of using only the dataset.
For machine learning in banking, the challenge with forecasting models is to find the optimal number of previous events and variables that should be considered when making future estimates. Too many or too few can result in inaccurate results. For predictions, it is important to select datasets that accurately represent the business. If the dataset contains transaction data going back 30 years, the algorithm is unlikely to generate the best results.
How do banks use machine learning and deep learning for forecasting and prediction?
Machine learning for financial forecasting can be applied to many administrative, operational, and client areas of the banking industry. Since financial institutions can collect vast amounts of data, machine learning algorithms can be applied to almost all banking business operations with great success.
Anomaly detection and fraud prevention
Cybercrime is costing consumers and businesses billions of dollars every year. Financial institutions spend billions more investigating and recovering the stolen money. There are more cyberattacks now than ever before, and they are becoming more and more sophisticated. Strong fraud prevention mechanisms such as forecasting using deep learning for banking are key to preventing unnecessary losses from fraud.
Banking forecasting using machine learning allows companies to monitor incoming transaction parameters in real-time. The algorithm examines the time series, evaluates customer actions, and examines other variables to determine how likely a suspicious transaction is to be fraudulent. The best way against costly fraud is early detection because it enables banks to block any questionable behavior, thus saving consumers’ money and preventing losses from compliance fines.
Using machine learning for forecasting in banking drastically reduces the amount of time it takes to spot suspicious transactions as these models can sift millions of data points every second.
Watch the SDK.finance demo video to explore how to simplify transaction management and ensure financial compliance with our powerful FinTech Platform. This video highlights how SDK.finance provides a comprehensive view and control over your client transactions, as well as the AML & fraud prevention functionality:
Risk assessment
Machine learning forecasting for banking enables more accurate reporting by automating credit risk testing for both banks and customers. By evaluating a consumer’s financial history, recent transactions, and purchasing patterns, machine learning can make accurate forecasts of future spending and income. Automated risk assessment enables banks to automatically offer the best possible terms for loans and credit products to customers based on their risk.
Credit risk forecasting for banking using deep learning takes minutes and eliminates human errors that can create unnecessary problems down the line. The same algorithm applies to investment risks as well so that banks can evaluate their assets to make better financial decisions. SDK.finance understands how important risk management is for financial companies. To help banks optimize their risk exposure and maximize revenue, SDK.finance offers a risk management feature.
Customer churn
It is much more expensive to win over a new customer than to retain an existing one. Customer churn is a vital metric that helps to identify and convince clients before they decide to switch services or products. Using deep learning for banking predictions, companies can determine behavior patterns exhibited by clients before they leave. Whether it’s less frequent visits to the platform’s application or lower transaction volume, banks that can spot that behavior are more likely to retain their customers.
The same metrics can be used to tailor the user experience to maximize retention. The Tech Giants have been using machine learning to tweak their website to maximize the time people spend there. Now financial institutions can do it as well with SDK.finance’s white-label platform that uses deep learning for banking predictions.
Business performance
Businesses generate an immense amount of data about their performance every day. Making sense of that data is a complex task that can be very rewarding when done right. Instead of letting valuable data go to waste, machine learning forecasting allows companies to forecast their business performance and account for seasonality, consumer sentiment, background, and external factors.
With accurate revenue forecasts, banks can plan investments and expenditures accordingly. If a branch or division is underperforming, machine learning can help to identify the cause and ways to correct it quickly. Planning infrastructure needs based on future forecasts can prevent unexpected downtimes and additional expenses. SDK.finance leverages machine learning to help companies break down the neverending raw data stream into actionable insights about their business performance.
