Managing Big Data and Big Risks in Banking

By Eric Crabtree, Global Head Financial Services, Unisys

Data is playing an increasingly important role within the banking industry. It is key to driving the development of intelligent omnichannel customer interactions, tailored to suit the needs of individuals and households. Data is also powering new technologies, such as AI and bots, which are in turn helping to improve operational efficiency and reduce risks. Data is even enabling new banking models, such as peer-to-peer lending, crowdfunding and the sharing economy.

The impact of data is best highlighted by looking at the advances in consumer credit. Traditionally, banks primarily rely on credit scores, which are based on a narrow range of slow-moving data points. This modeling approach brings about two major constraints. First of all, decision-making is slow due to banks having an incomplete view of a consumer’s financial health. And second, this creates ‘thin files,’ especially on millennial consumers who lack a financial history and have an aversion to debt. In fact, in many countries, the use of consumer credit has been solely negative, whereby banks use the model to essentially blacklist people who have made late payments.

Today, banks are basing their lending and risk management decisions on integrated data. Debt repayment information is being combined with near real-time transactional and account balance data to build thorough risk assessment models. Instead of relying on the timeliness of payments or the percentage of available credit used, they can assess risk patterns from past behavior to sense future changes.

With ‘thin file’ consumers, banks and credit reporting agencies are leveraging new data sources, such as bill payment history and mobile phone usage. In some cases, particularly with non-bank lenders, the nature of a consumer’s social media network can also contribute to assessing credit worthiness.

Data can be used to tailor sales and marketing interactions. In the same way that it helps banks form a detailed view of a consumer’s credit worthiness, it can also be used to customize sales messages and products for the benefit of increasingly service-savvy customers.

Acting on Data

Often, the volume, velocity and range of data types can technically exceed the capabilities of traditional technologies (e.g., relational databases). Unstructured data, such as video, voice and text, are particularly unsuited to former IT approaches and first generation Big Data technologies.

To combat this, banks are adopting machine learning, where predictive models continually train themselves based on streams of data. Machine learning can help identify nuanced details for improved results. For instance, traditional regression or decision tree approaches may predict which customers are likely to churn based on relevant variables. Machine learning goes beyond linear relationships to recognize interactions across much broader sets of data.

Success will rely on cultural change. In light of fast-moving data and the increased pace of change in expectations, a much more iterative approach to planning is required. In particular, the move to agile product development requires a significant shift in product management style.

The Challenge to Banking

Inevitably, the central role of data brings about new risks. People need to understand how to govern and organize for an analytically-driven business. For example, many banks currently only keep data on people who successfully apply for credit. By definition, including only this subset, rather than all who applied, means banks are at risk of reducing their marketing opportunities with new prospects.

However, the most acute risks may be external. Cyber threats are damaging more than just reputation these days and are actually leading to the removal of CEOs, as is the case with Target and Sony. Similarly, senior government and academic leaders are also losing their jobs in response to data breaches. The nature of threats has changed as well, with hackers now seeking physical effects or attempting to discredit an organization by subtly corrupting, rather than stealing its data.

Solving for the Future

In order to remain competitive, banks need to ensure they have the best security technology at their disposal.

This includes authentication, such as the use of biometric technology that can confirm a consumer’s identity. This is centered on an understanding of usage habits, for example, a person’s unique way of holding a phone, their typing speed and the angles at which they swipe their finger.

Separately, machine learning can be applied to detect threats. For example, a cyber analytics model can continually ingest large streams of network activity data to define activity baselines and detect anomalies. These models can be applied within an organization’s cyber security software, as well as integrated with threat intelligence.

This ability to protect customers is based on continuous innovation. And the ability to understand and anticipate the evolving nature of cyber threats worldwide is critical for banks to ensure future success. With the proper technology, banks can ensure that their tomorrow is secured.