How Data Science Plays a Key Role in Mitigating Risks in Banking and Financial Institutions

In a dynamic crucial business landscape, risks and challenges naturally originate when we experience a technological or cultural shift. From financial uncertainties to disasters (both manmade and natural), a variety of risks hamper the growth of an organization. Adopting a data-driven approach powered by data science tools can allow identifying and mitigating risk factors without experiencing financial and reputational losses.

Particularly, in any banking and financial institution, a proper risk management program is very crucial. All organizations confront a range of hazards, and risk management aims to maximize a company's return on investment while minimizing its losses. Anyone in the risk management field is familiar with data analytics. Risk management is heavily reliant on data, and risk managers have been employing analytics for many years. Financial institutions are the epicenter of data and were early adopters of data analytics. However, as we've seen, data has increased in the previous decade, and big data tools have evolved to aid in the analysis of these massive datasets.

According to the State of Enterprise Risk Management report, between 2016-2019, 62% of businesses faced a critical risk incident. These organizations experienced a major impact on their employee productivity (62%), employee safety (29%), operational efficiency (59%), reputation (28%), and competitive edge.

Keeping the above data as a reference, it can be assumed that employee productivity and operational efficiency are hampered in almost every organization experiencing a risk-based incident.

Using a well-planned data science and analytics approach, we can identify, eliminate, and monitor impending risks and save millions of losses.

Basics of Data Science

Data Science is the art of generating data analytics by gathering insights and valuable information from a multitude of resources. This technology uses raw data that your organization collects and prevents any future-based risk events with valuable information.

Data Science was first introduced in 2008 when the need to process and utilize unstructured data drastically increased. Over the next decade, (and even till the present date), we used principles of data sciences to understand data, process raw resources, find value in this data, and visualize/communicate it in an understandable format.

Basics of Risk Management

Businesses face certain risks and challenges during normal operations. This includes financial losses, accidental risks, or natural disasters. For example, a natural disaster damaged your data center and erased your data.

These risks create a domino effect causing productivity dips, reducing operational efficiency, causing monetary damage, and reputational loss.

For this reason, risk management is crucial for the growth of a business. Using data science in risk management allows the identification of looming threats and helps minimize the impact of risks on the organization.

Using Data Science for Risk Management in Banking and Financial Institutions

Traditionally, insurance plans were used by businesses to manage risks in the workplace. However, with time, the extent, impact, and size of risks have evolved. Businesses now experiencing bigger risks involving more money and physical/financial damage. As a result, there is a need for a data-driven methodology to mitigate organizational risks and reduce damage related to operational efficiency, governance, and productivity.

1. Identification

   Businesses experience both internal and external risks. For example, internal inadequate processes, funding issues, etc., and external climate change, macro-economic changes, regulations, etc.

   These external and internal risks hamper the company’s growth and interfere with the goals and objectives of the business.

   However, collecting huge volumes of data from a variety of sources gives an unprecedented amount of information to businesses. Using data science in risk management with this information can help identify looming risks. The available data empowers business stakeholders to identify different patterns, which helps know risks that are gradually developing in the system.

   For instance, a banking institution or financial organization collects and produces data related to customer transactions, user journeys, financial data, and market fluctuations. Data science can be utilized to detect hidden risks in market trends and fluctuations. Businesses have data from discrete and disparate sources creating a huge resource of unstructured and unprocessed information. Using data science, in this case, helps analyze potential risk factors like sudden steep market fluctuations to eliminate risks and react quickly.

2. Prioritization

   While identifying business risks is the first step toward eliminating challenges that you might face in the future, it is imperative to prioritize risks. After the above identification, it is likely that businesses are able to find out the financial risks that are critical and non-critical.

   How can businesses prioritize risks?

   With the help of available data, it is possible to extend risk assessment and align indicators for risk profiling. This activity will help know the impact, nature, and likelihood of risks. Ideally, data science can empower a business to detect looming risks, know their financial consequences, and assess the degree of impact. This knowledge can allow better investment in mitigating risks correctly from the system.

3. Repetitiveness

   An important factor that can be assessed with risk profiling is the repetitiveness of the risk. Will the risk continue to appear? What is the severity of a particular risk?

   This helps to analyze closely the implications of the risk, and evaluate the profit/loss associated with the risk, depending on the frequency or repetitiveness of a particular risk.

   With this, businesses are also able to prepare for future consequences, and they become more equipped to deal with challenges. Data sciences play a major role in helping organizations analyze the incident occurrence and frequency for a deeper understanding of specific business areas that may be affected.

4. Mitigation

   Organizations may have already identified risks and assessed the severity to form a priority for mitigation. In the mitigation phase of enterprise risk management, it is necessary to find a suitable solution according to the consequences of the risk. For this, a multitude of risk modeling methods can be used, such as the ‘what-if’ scenarios that allow businesses to evaluate past situations and predict future outcomes.

   How data science in risk management helps in this stage?

   Data science is utilized to track past and predictive data, which allows knowing the potential of the mitigation method. This is to stay businesses can find the effectiveness degree of the solution with the available data. In this way, it may become possible to succeed in risk mitigation without experiencing further challenges.

   Consider operational risk in financial systems at this stage. It involves fraud activities, regulatory violations, and rogue trading practices. The traditional methods used to detect and manage risks may require significant time, which is enough for committing fraud or counterfeiting. Data science helps capture data to a single stream, and observe transactional patterns to identify, detect, and mitigate fraud before it happens.

   Think about it, this ability of data science to help bank managers understand data patterns also ensures the building of a predictive system for borrowers. For instance, using the loan history, income, demographics, and transactional success of the borrowers, businesses can predict customer behaviors in the future. According to this analysis, risk indicators can be closely considered before offering loan services to some high-risk customers.

5. Monitoring

   An enterprise environment is dynamic, and it changes when the market shifts. As a result, risk factors also evolve with time. Therefore, risk monitoring is the key to identifying financial risk indicators and mitigating challenges to avoid consequences.

   For this, businesses consistently monitor risk parameters and measure the severity of the situation. With the help of data sources and parameters, it is also possible to automate reminders linking them to unusual data behavior for effective risk control. These automated alerts allow financial risk managers to take action on time.

   With the help of the above approach in data science, anti-money laundering systems can be set up in banks and financial institutions. The industry needs an advanced method to quickly detect money laundering trends and create an alarm. Data science, as discussed above, is able to effectively create an alarm for suspected fraud activities. Businesses can utilize unstructured and structured data to find hidden transactional patterns that are suspicious and may lead to fraud. The ability of data science to generate real-time valuable insights can help us create alarms for suspected fraud situations.

6. Reporting

   Risk reporting is an unavoidable stage of risk management for multiple market and political factors. Businesses need to report risks so that further analysis can be conducted on the nature of risks. This involves effectively monitoring these risks based on dynamic factors that change for every organization.

   Data science can be implemented at the data feed stage in this case. Creating a pipeline involving collecting data on enterprise financial risks and learning from past behaviors can aid risk management. Data science can help feed data to this stream for future risk prevention.

Conclusion

Data science is a tool that helps businesses in banking, financing, and other industries to quickly identify and mitigate impending risks. Businesses are able to dynamically monitor new risks and evolve their business structure such that it can averse consequences.

Innominds offer data intelligence services that combine artificial intelligence and business intelligence. These services empower businesses with features, including data transformation, adaptive dynamics, and augmented analytics, which offers improved functionality, operational efficiency, and smart decision-making power.

Innominds was able to eliminate risks and manual errors in the system by 80% for a global consulting business. The customer was looking for support in the identification, remediation, and reviewing of tax data that was collected from disparate sources. Innominds data intelligence services helped them eliminate manual structures and automate several processes, which helped the client to decrease workload by around 90%.