Predictive Analytics Brings Both Positive Changes to Financial Industry


Predictive analytics is changing the future of capitalism in the most surprising ways. One study found that the market for this technology will be worth $23.9 billion by 2025. One of the fields that has been most influenced by predictive analytics is the financial industry. Predictive analytics is the core of financial business intelligence.

The financial sector has undergone some major transformations, which are the result of unstoppable advances in data innovation. Digital financial products and services are increasingly being adopted by customers to change their habits and utilize the various channels that banking institutions offer today. Digital media has become the preferred means for customers to interact with their bank, so many customers have given up even going to their local branch.

Predictive Analytics Turns the Financial Industry Upside Down

Joris Lochy, a renowned Fintech expert, has talked about the role of big data in the financial sector. He points out that this is arguably the most data intensive industry in the modern economy. This has given them countless opportunities to take advantage of financial business intelligence.

Lochy points out that the sheer amount of data has enabled financial institutions to create a number of predictive analytics models. However, they need to use this technology wisely to get the full benefit from it.

Predictive analytics need to focus on customer needs. Customers demand new ways to interact with their bank with the same ease they communicate with a friend through a social network or access their e-mail. This facility is made possible by predictive analytics technology. A number of new players have appeared in the Fintech sector, which, unlike many banks, offer clients this simple functionality. They use a wide number of financial business intelligence platforms and new predictive analytics algorithms to anticipate the needs of their customers and deliver the services that they will depend on.

These financial organizations offer services that are more efficient and comfortable for their clients. Many factors have converged that have caused traditional banking institutions to adapt their own predictive analytics solutions. They use innovative, data-driven strategies that focus on the client and offer personalized attention. In this context, the traditional banking sector must take advantage of the data available to its clients, so that through Big Data and Artificial Intelligence techniques can be used to offer private and personalized banking services to the client.

Predictive Analytics Streamlines Advertising in the Financial Sector

Financial institutions need to utilize predictive analytics to reach customers more efficiently. There are a number of ways that predictive analytics enables them to improve the ROI of their advertising campaigns.

We could define Big Data as the capacity to store and analyze large amounts of information quickly. It can often be processed in real-time. This is important for optimizing advertising strategies, because recent data is essential for creating advertising campaigns that are governed by predictive analytics.

The techniques behind modern Artificial Intelligence algorithms allow us to extract the most valuable information and employ it effectively. If banks are able to develop strategies to handle Big Data, they will be at the forefront of emerging digital business models (fintech), and not only that, they will have a competitive advantage in an increasingly consolidated market.

These financial institutions have much more data than their competitors, which helps them create predictive analytics strategies that give them a cutting edge. The bank knows the movements of its clients: what they do, where and when (traditional structured information). They also use unstructured customer data, such as information from social networks, geolocalization, Internet activity the products they purchase and much more. They use this information to compose a large database that allows them to know exactly each of their clients will be integrating Big Data into the financial entity and benefit from Artificial Intelligence. The banks will be able to offer clients personalized services and access to better private banking features, which help them earn greater customer satisfaction and loyalty. Banking is the most appropriate sector to take advantage of Big Data and the time is now.

The traditional business model of the financial sector has become obsolete in light of predictive analytics advances

The traditional business model of the financial sector has become obsolete, as banks are forced to change their business models to reflect advances in predictive analytics. They must develop new skills to offer customers tailored products and services, which can be consumed in ‘one click’ and provide them with all the necessary information to make appropriate decisions. They should be able to do so in advance, by being able to anticipate the specific needs of the customer.

To achieve this, the bank must have a 360º knowledge of the customer. The bank’s marketing model must be totally transformed towards this context, which is based on the customer’s behavior and learning as its activity progresses. The marketing department must move from offering 3 or 4 new offers a year with costly promotional campaigns to making 10 to 15 offers a month, which are highly personalized, easily executable (very few clicks) and very simple to understand. This personalized and private banking service has been called Banking 3.0 in the sector. It is one of the biggest ways predictive analytics is deployed in the financial industry.

Case Studies of Predictive Analytics in the Financial Industry

Many banks are launching different Big Data initiatives that, although they may still be incipient, are disrupting the industry. These initiatives are the following:

  1. The development of personalized products for one of the bank’s customers. This requires the creation of a predictive analytics model with the customer at the center of the business interface. The algorithms focus on analyzing his behavior and what features he depends on in each channel. It learns and adjusts models, in order to offer him personalized products in advance.
  2. New business opportunities for the bank’s customers. By analyzing the customer’s external information, the bank can discover new business opportunities. For example, by analyzing the customer’s social networks, it can detect the customer’s intention to buy a car or take a trip. The bank can respond to this information by offering the customer the desired product channeled through the bank, charging a commission for removing the customer from the management of the acquisition. The bank can extend its business model to a ‘pseudo travel agency, ‘pseudo real estate’ or ‘pseudo car buying/selling management company’.
  3. New business opportunities for non-customers. Banks can find new business opportunities for non-customers and the short-term objective would not be to gain their loyalty or even encourage them to open an account. The bank can offer them a product that meets their current needs. In this case, the bank interested in the acquisition is at a disadvantage because it does not have all the data and banking movements, so, again it is necessary to resort to external data, publicly available to detect intentions, specific needs or events that occur in their lives, such as the need for a micro-credit to support the reform of their house.

How digital is the financial industry and to what extent are analytics tools already used? A recent study in Germany examines current developments and assessments with a focus on digitisation and data usage.

German banks and financial institutions are undergoing digital change and are regularly confronted with new regulatory challenges. Ad-hoc inquiries and reporting are a central requirement in this context and could be simplified by the use of data analytics tools.

The management consultancy Deloitte has investigated possible uses of analytics tools, which are necessary prerequisites for leveraging customer data in the financial industry. Assessments and expectations of top management and IT executives of German financial institutions were obtained in a study. The participating institutions range from German private banks, asset managers and Landesbanken to public law institutions.

Where do German financial institutions stand in the field of data analytics?

German credit institutions have very large holdings of customer and transaction data, but tend to only use this data for rudimentary purposes. The full use of these data sets offers profitable opportunities for sustainable growth.

New technologies and methods in the field of analytics open up efficient ways for financial institutions to use data. 97 percent of those surveyed believe that analytics methods and tools help to improve and accelerate processes for data collection, preparation and validation. The main areas where these tools can be used are portfolio risk assessment, cost analysis and data quality improvement.

Central banking analytics tools are of particular importance, as they draw on the entire pool of information available in a financial institution. However, the majority of German financial institutions state that they can only generate the data for reporting purposes with a great deal of effort or very long preparation times (e.g. with classic Excel evaluations).

Complexity of data budgets makes it difficult to use analytics tools

Many banks and savings banks want to have an integrated and consistent data budget at all times. However, this is not an easy goal to achieve. In addition to the high complexity of such data warehouses, the quality of the existing data has not yet been sufficient.

The financial effort required to overcome these obstacles is considered extremely high. Only one in four of the German financial institutions surveyed currently considers itself to be professionally and technically capable of introducing an analytics tool independently. Current analytics tools can be based on existing data stocks or various data sources and, when implemented in a relatively short time, can contribute to a sustainable improvement in data quality.