AI News, Top 9 Data Science Use Cases in Banking

Top 9 Data Science Use Cases in Banking

Using data science in the banking industry is more than a trend, it has become a necessity to keep up with the competition.

Image source: The Daily Star An example of efficient fraud detection is when some unusually high transactions occur and the bank's fraud prevention system is set up to put them on hold until the account holder confirms the deal.

For new accounts, fraud detection algorithms can investigate unusually high purchases of popular items, or multiple accounts opened in a short period with similar data.

But rather than viewing this as just a compliance exercise, machine learning and data science tools can transform this into a possibility to learn more about their clients to drive new revenue opportunities.

After that, being armed with information about customer behaviors, interactions, and preferences, data specialists with the help of accurate machine learning models can unlock new revenue opportunities for banks by isolating and processing only this most relevant clients’ information to improve business decision-making.

Investment banking evaluates the worth of companies to create capital in corporate financing, facilitate mergers and acquisitions, conduct corporate restructuring or reorganizations, and for investment purposes.

The importance of this measure is growing fast, as it helps to create and sustain beneficial relationships with selected customers, therefore generating higher profitability and business growth.

First, a large amount of data must be taken into account: such as notions of client’s acquisition and attrition, use of diverse banking products and services, their volume and profitability, as well as other client’s characteristics like geographical, demographic, and market data.

There are many tools and approaches in the data scientists’ arsenal to develop a CLV model such as Generalized linear models (GLM), Stepwise regression, Classification, and regression trees (CART). Building a predictive model to determine the future marketing strategies based on CLV is an invaluable process for maintaining good customer relations during each customer’s lifetime with the company that results in higher profitability and growth.

The potential value of available information is astonishing: the amount of meaningful data indicating actual signals, not just noise, has grown exponentially in the past few years, while the cost and size of data processors have been decreasing.

To build a recommendation engine, data specialists analyze and process a lot of information, identify customer profiles, and capture data showing their interactions to avoid repeating offers.

Outstanding customer support service is the key to keep a productive long-term relationship with your customers. As a part of customer service, customer support is an important but broad concept in the banking industry.

In essence, all banks are service-based businesses, so most of their activities involve elements of service. It includes responding to customers’ questions and complaints in a thorough and timely manner and interacting with customers.

To gain competitive advantage, banks must acknowledge the crucial importance of data science, integrate it in their decision-making process, and develop strategies based on the actionable insights from their client’s data.

This list of use cases can be expanded every day thanks to such a rapidly developing data science field and the ability to apply machine learning models to real data, gaining more and more accurate results.

Top 9 Data Science Use Cases in Banking

Using data science in the banking industry is more than a trend, it has become a necessity to keep up with the competition.

An example of efficient fraud detection is when some unusually high transactions occur and the bank’s fraud prevention system is set up to put them on hold until the account holder confirms the deal.

For new accounts, fraud detection algorithms can investigate unusually high purchases of popular items, or multiple accounts opened in a short period with similar data.

But rather than viewing this as just a compliance exercise, machine learning and data science tools can transform this into a possibility to learn more about their clients to drive new revenue opportunities.

After that, being armed with information about customer behaviors, interactions, and preferences, data specialists with the help of accurate machine learning models can unlock new revenue opportunities for banks by isolating and processing only this most relevant clients’ information to improve business decision-making.

Investment banking evaluates the worth of companies to create capital in corporate financing, facilitate mergers and acquisitions, conduct corporate restructuring or reorganizations, and for investment purposes.

The importance of this measure is growing fast, as it helps to create and sustain beneficial relationships with selected customers, therefore generating higher profitability and business growth.

First, a large amount of data must be taken into account: such as notions of client’s acquisition and attrition, use of diverse banking products and services, their volume and profitability, as well as other client’s characteristics like geographical, demographic, and market data.

Building a predictive model to determine the future marketing strategies based on CLV is an invaluable process for maintaining good customer relations during each customer’s lifetime with the company that results in higher profitability and growth.

The potential value of available information is astonishing: the amount of meaningful data indicating actual signals, not just noise, has grown exponentially in the past few years, while the cost and size of data processors have been decreasing.

There is no need to prove that such segmentation of clients allows for the effective allocation of marketing resources and the maximization of the point-based approach to each client group as well as selling opportunities.

To build a recommendation engine, data specialists analyze and process a lot of information, identify customer profiles, and capture data showing their interactions to avoid repeating offers.

To gain competitive advantage, banks must acknowledge the crucial importance of data science, integrate it in their decision-making process, and develop strategies based on the actionable insights from their client’s data.

This list of use cases can be expanded every day thanks to such a rapidly developing data science field and the ability to apply machine learning models to real data, gaining more and more accurate results.

Machine Learning

Supervised learning algorithms are trained using labeled examples, such as an input where the desired output is known.

The learning algorithm receives a set of inputs along with the corresponding correct outputs, and the algorithm learns by comparing its actual output with correct outputs to find errors.

Through methods like classification, regression, prediction and gradient boosting, supervised learning uses patterns to predict the values of the label on additional unlabeled data.

Popular techniques include self-organizing maps, nearest-neighbor mapping, k-means clustering and singular value decomposition.

Analytics in banking: Time to realize the value

By establishing analytics as a true business discipline, banks can grasp the enormous potential.

Some executives are even concluding that while analytics may be a welcome addition to certain activities, the difficulties in scaling it up mean that, at best, it will be only a sideline to the traditional businesses of financing, investments, and transactions and payments.

Business leaders today may only faintly remember what banking was like before marketing and sales, for example, became a business discipline, sometime in the 1970s.

A look around banks today—at all the businesses and processes powered by extraordinary IT—is a strong reminder of the way a new discipline can radically reshape the old patterns of work.

The availability of information is booming: in the past few years, the amount of meaningful data—true signal, not noise—has grown exponentially, while the size and cost of processors decreased.

We are well past simple linear regressions—machine learning now features support vector machines, random forests, gradient boosting, and many other astonishing algorithms.

Any company’s ability to perform these analytics has been significantly boosted by the exponential increase ofcomputing power (which makes it possible to undertake, in just seconds, an analysis that in the past would have taken weeks) and by new data-storage technologies, such as Hadoop.

Put it all together, and you get advanced analytics: industrial-scale solutions to exploit data for authentic business insights and vastly improved decision making.

Rich real-time data—numbers, yes, but also text, voice, and images—now exist for literally every action that customers make, every product that banks sell, and every process that banks use to deliver those products.

A good many are “started but stuck”: they have invested significantly in data infrastructure (mostly as a result of regulation) and experimented with advanced-analytics techniques (mostly through specialized teams loosely connected to the corporate center).

Tactically, we see banks making unforced errors such as these: Avoiding the pitfalls and accessing the broad set of opportunities requires CEO leadership as banks develop two assets: a strategy for the transformation and a robust analytics organization to assist and empower the businesses as they learn to use analytics in their everyday work.

That’s because we think every institution, unless its circumstances are extraordinary, should set the same aspiration: to establish analytics as a business discipline—the go-to tool for the thousands of decision makers across the bank.

Business-improvement levers (such as dynamic and value pricing, credit underwriting, sales-area planning, yield and claims management, fraud detection, call-center routing, and workforce planning) are also relevant for most banks.

While the first couple of use cases can be introduced top-down or outside-in, it is just as important to encourage everybody in the bank to become creative and make suggestions—while always ensuring a clear path to creating value.

It can then work through a set of five steps: identifying the source of value, considering the available data (easier to do with a data lake, as we describe in the sidebar), identifying the analytics technique that will respond to the problem and probably produce insights, considering how to integrate analytics into the workflow of the business, and anticipating the problems of adoption (Exhibit 2).

Theclassical steps of successful change management will be essential: role modeling the new behavior, clearly explaining why change is needed, building the skills of the businesses so they can succeed with the new tools, and reinforcing the bank’s commitment through formal mechanisms (such as incentives).

An analytics center of excellence, the spine of such a system, will probably need some or all of the following components: More than 90 percent of the top 50 banks around the world are using advanced analytics.

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