Why Predictive Analytics in Banking Is Transformational

The banking sector struggles with two contradicting challenges. First, it needs to adopt the latest technologies to stay competitive and fight fraud. Second, it still has to hold on to legacy systems, frequently those on-premises, that offer stability and a certain degree of protection.

Clients are also putting more pressure on financial service providers, as they request more personalized solutions, easy-to-access products, and an overall better customer experience in line with strict security and privacy standards. With this ongoing disparity between client demands and banking goals, predictive analytics can help at numerous touchpoints and streamline more than a few operations.

Marmuzevich Alexander, CTO of InData Labs, comments:

That's why Google and Facebook as the most skilled data collectors are so successful. And banks that own customer processing data can afford to sell predictions of customer behavior. They can begin to consider predictive analytics as quite a separate business. It is possible to predict almost anything that isn't really random and in case there is enough data to build predictive models.

Initial Applicant Screening

Inspired by HR technology helping recruiters sort through thousands of applications while showing high accuracy, professionalism, and steadiness, the banking industry is also continually looking for new ways to screen applicants more fairly and quickly.

Predictive analytics can offer a solution to this problem by grouping applications into risk classes. The downside of this approach is that since the algorithm acts as a black box, it can rule out valuable clients if they don’t fall into a particular pattern.

Credit Score Models

The FICO score has been refined continuously over the past thirty years to include ever more relevant variables. The entire logic behind this number, which governs credit institutions’ and even employers’ decisions about applicants is based on predictions. It shows the propensity of a person to be a trustworthy client or a diligent employee.

One of the direct uses of this score is to predict how big is the “propensity to default” (PD) in the following 90 days. For example, while somebody with a score of 700 has just a 5% PD, an applicant with a score of 520 has 46%.

Fraud Detection and Prevention

Banking fraud is on the rise; a recent survey by KPMG shows that at a global level over 60% of the respondents experienced an increase in fraud. The most common banking fraud typologies include scams, phishing, and data theft through social engineering.

Predictive analytics can help identify potential fraud by analyzing the most common operational patterns regarding trades, purchases, and payments. This works with both structured data (transactions) and unstructured data (emails, reviews, forum entries) to uncover hidden patterns. Some banks have monitoring systems in place that scan data continuously; other systems are triggered by specific actions or the results of sampling analysis.

Planning Personal Finances

Machine learning models are great at learning patterns and detecting any changes in them. As a person’s monthly expenses usually follow a regular pattern, a predictive analytical model could help clients identify their main spending categories, primary money sources, as well as cash flow trends.

Such a model could alert the user if they are running low on cash or send reminders to avoid late payments and penalties. If authorized by the client, the system could automatically make transfers between different accounts when necessary.

This approach could also help banks with client segmentation. For example, those who are running low on cash just before the payday could be offered express loans at better rates considering that it is only a matter of a few days. At the same time, clients with a consistent track record of regular purchases of items like gadgets or travel packages could benefit from products and discounts explicitly designed for them.

Risk Hedging

While most clients repay their loans on time, banks have to deal with those who have a default risk and identify them as soon as possible.

Red-flagging certain clients before they miss three or more payments can help banks save money by offering support and financial guidance before the debt becomes overwhelming. Since prevention is better than reactive behavior, using such analytical tools helps banks build their portfolios and hedge risks by either setting a higher interest rate or offering a new payment schedule.

Computing Customer Lifetime Value

Every organization is interested in identifying clients with a high potential of becoming loyal. Companies are more than happy to offer these clients discounts and special offers, as the cost of acquiring a new client exceeds that of promotions.

Predictive analytics looks at the lifestyles, spending patterns, as well as other variables and computes the probability of a client to buy products other than the ones they initially signed up for. Credit institutions could offer a car purchase loan or life insurance to a client who already take out a mortgage.

Better Marketing Strategies

Since financial services are hard to differentiate by provider, marketing efforts get more laborious. They require well-targeted messages delivered at the right time.

Predictive analytics can help with this task by identifying those clients who are most eager to buy a product. For example, if a client has several loans which they find hard to keep track of, they might be the right candidate for refinancing, in order to replace all of their existing payments with a single one.

Challenges of Predictive Analytics

Predictive analytics works by looking for patterns in everything and ruling out outliers as problems. While in most cases this could be a safe and sound approach, it could be highly detrimental in specific cases. It can contribute to making the rich richer and the poor poorer by not granting access to financing instruments for risk-hedging reasons.

That’s why companies looking to implement predictive analytics need to pay extra attention to the quality of data fed into their predictive models, and follow regulatory guidance to avoid compliance issues.