How to address banking risk in a digital world

What are the major types of banking risks?

Counterparty credit risk

Counterparty credit risk refers to a situation where the counterparty in a transaction does not honor their side of the contract. The counterparty could be a borrower who does not pay back a loan or a defaulter on a credit card. The credit risk could also evolve when a contractual obligation is not met. For example, if a bank has insured itself against market risk and if the insurance vendor does not honor the contract, it’s a counterparty credit risk. In simple terms, counterparty credit risk can happen whenever a bank enters a contract.

Operational risk

Operational risks can arise due to an error or damage caused by people or systems. It might be unintentional like a teller accidentally typing a wrong transaction amount. It could also arise due to glitches in the system or through sabotage. Banking fraud is another way an operational risk can arise. Banking fraud often arises due to compromises in cybersecurity.

Market risk

When a bank invests in the capital market, it is susceptible to market risks. Volatility in the equity market might arise due to various reasons—global financial and political events, change in commodity pricing, and change in interest rates. By investing in the equity market, the bank exposes itself to a plethora of risks.

Liquidity risk

Liquidity risk arises when a bank is unable to liquefy its assets to meet contractual obligations. For example, if a bank is unable to provide money to customers who want to withdraw their deposit, it is a liquidity risk. This liquidity risk has a snowball effect. When one customer is denied a fund transfer, other customers are alerted, and more customers rush to withdraw money. Liquidity risk can also arise when a bank is unable to liquefy its investments in the stock market. Banks cannot simply sell all their stock holdings as there are regulations to protect the market.

Non-financial banking risks

In addition to various financial risks, there are several non-financial banking risks. Technology risk is one of them. It includes cybersecurity risks, the risk of non-compliance with data protection regulations, and the risk of legacy systems. While banks develop thorough plans for dealing with financial risks, they may not be aware of technological risks. Banks might face several challenges when they try to mitigate technology risks.

What are the major challenges in mitigating technology risks?

Data governance

Banks can be overwhelmed with the influx of massive volumes of data from several sources: customer information, financial transactions, sales and marketing data, and unstructured information in the form of text messages, emails, and social media interactions. With the increase in enterprise data, it is a challenge to ensure data quality and security.

Several banking risks may arise due to a lack of data governance plans. Financial organizations often have data in disconnected silos and teams making decisions based on partial data.

Real-time analytics

Analyzing data in real-time can help risk management immensely, but it comes with several challenges. One of them is the accuracy of the real-time data. Before it can be used for any decision-making, the data should be cleaned, verified, and authorized.

Another challenge in real-time analytics is effectiveness. Even if a financial organization is able to provide high-quality, real-time data, it may not be able to make quick decisions based on this data.

Banks often struggle with increasing the quality of real-data and enabling quick decision-making based on this data.

API management

There are a new set of challenges with open banking (where a financial organization allows third-party APIs to access their financial services). The first challenge is the security risks related to exposing the banking functions to APIs. Cyber-attacks on these APIs can pose a severe risk to banking systems. Another challenge is the financial liability due to a compromised API. In the current system, when there is a cyberattack on the API, the financial institution must bear the liability for the customers’ losses. To overcome these challenges you need an innovative API Management platform.

Data virtualization

In many financial institutions, data resides in silos. It is challenging, or impossible, to make data-based decisions when there is no centralized data repository. Data virtualization solves this issue by combining all the enterprise data and providing a logical interface for data access.

Data virtualization can be a challenge for financial organizations as they deal with an enormous amount of partially structured and unstructured data. With new data regulations, the collection and storage of data must comply with laws and legislation.

Data science and integration

Models based on data science help banks make better and more informed decisions. It helps them to respond to the market faster and beat out competition.

However, there are several challenges related to data science models. Often, there is no centralized repository for the financial institution’s models, and it is hard to track which model has been used in risk management functions. Tracking the efficiency of the data science models is also a challenge because teams may develop these models with different programming languages and technologies, making these models obscure.

What are the key trends in risk management?

The risk management function of banks has changed considerably in the last decade. While it is difficult to predict how it will continue to change, there are some key trends that will define risk management in the future:

Rapidly changing regulations

Banking regulations are becoming stricter each passing year. Each country has its own set of regulations that change according to the economic environment. Activities of financial institutions and their relationship with the customers are under strict scrutiny. Due to the ever-changing nature of regulations, financial institutions are looking for a more flexible risk management function.

The rise of fintech and customer expectations

With the entry of advanced technology into the banking sector, customers expect faster and better services. There is great competition in the banking sector to cater to changing customer needs and demands. Online banking and applications open up a new source of risk for banks.

Evolving technology and analytics

The future risk functions should leverage advancements in technology like big data, machine learning, artificial intelligence, and improved analytics. These technologies allow risk functions to make better decisions. They also help create a data infrastructure that enables enterprises to spend more time analyzing their data rather than managing it.

The emergence of new risks

Banks face new types of technological risks. One example is the model risk, which evolves from an organization’s dependency on a business model. Cyber risks increase as banks go online and provide their services through third-party APIs. Hacking and banking fraud is increasing, and the risk function should be designed to take into account these new types of risk.

Security trends suggest that the future banking risk functions should be high performing. They should be able to handle a variety of risks and also comply with ever-changing regulations. The risk function should also adapt to the rapidly changing global economy. Only a fully-digitalized risk function with the following attributes can prepare a financial organization for future challenges:

• Automated risk evaluation and decision-making
• Utilization of advanced analytical models
• Integration with efficient data governance models
• Reliance on intelligent data science models

Improved data governance: a key opportunity

Data governance helps organizations protect and manage their data to derive great benefits. The availability of high-quality, reliable data across the organization will ensure faster and better decision-making. An organization-wide change is required to ensure more efficient data governance. This includes defining roles and responsibilities and ensuring more effective communication across the organization. Training employees and defining accurate processes for handling enterprise data helps mitigate banking risks.

Financial institutions can derive value from their data governance policy in many ways. At first sight, data governance might look like a tool for ensuring regulatory compliance. Financial regulations are changing often and are getting higher penalties for not complying. From the regulatory perspective, data governance is an important paradigm to ensure compliance.

Interestingly, more than just compliance, banks are looking to derive business value from data governance. An improved data governance plan helps the senior management in the organization become more aware of data to make informed decisions. It also ensures proper ownership of the data. A proper data governance plan also helps financial organizations monetize their data.

Creating a digital risk function to address banking risks

In banking, a risk management function includes plans for minimizing losses and liabilities. In the current financial market, a risk function is challenged by various factors:

• Ever-changing monetary policies: Countries often update and amend their monetary policies, and the banking risk function should adapt to these volatile policies.
• Volatility in regulatory demands: The laws that govern the operations of banks are changing very rapidly.
• Retreating from globalization: Countries are now scaling back from globalization to more nationalized priorities. This results in each country creating more specialized regulatory requirements.
• Economic uncertainty: Various minor and major recessions have hit the world economy. The volatile stock market adds to the uncertainty of the global economy. Maintaining a static risk function in this highly volatile environment is a great challenge.
• Introduction of fintechs: Fintech companies combine finance and technology to cater to changing customer needs. The introduction of technology into finance has introduced a new set of challenges for banks: an increase in transaction volume, third-party APIs, and cyber-attacks.

To address these challenges, banks require digital transformation.

What are the challenges for the digital transformation of risk function?

For a fully-digital risk function, various components of the enterprise have to work together. This includes people, processes, and systems. Without all these components, a digital risk function will not be effective. Enterprises often have processes and people who can support digitalization, but they may not have proper systems or the existing systems need upgrades. Low data quality and insufficient data infrastructure often impedes digitization efforts. Lack of data transparency is another challenge.

These challenges, coupled with ineffective data governance, might slow down the digital transformation banking risk function and cause misconduct. In addition, regulatory changes are forcing financial organizations to reengineer their data infrastructure.

The technological advances in data science can help financial organizations overcome these challenges. One major advance is improving data quality. Financial institutions can use advanced technologies to ensure that decision-makers, like the board of directors, always have access to accurate data. This will lead to better decision-making and reduce the chances of bank misconduct.

What are the technologies that strengthen the risk function?

A digitalized risk function helps financial institutions monitor their enterprise more efficiently. It also enables the organization to comply with changing regulations and steer clear of banking misconduct. A digital risk function greatly improves the quality and efficiency of risk decisions. With data governance, master data management, data science, and analytics, organizations can create insight into their risk function. These technologies help the data infrastructure of an organization.

• Master (or reference) data
• Metadata
• Transactional data.

Along with the digitalized risk function, an organization can use the following technologies to increase the business value of its data:

Data governance

With an efficient data governance plan, data ownership and quality can be monitored and managed. The data governance infrastructure also acts as a repository for policies, business definitions, and other metadata, including streams, models, reports, and dashboards. Data governance ensures that decision-makers have the right data and references.

Data sciences

Data science teams develop models or algorithms to work on the enterprise data. These models include analytical models, benchmarks, machine learning, and artificial intelligence models. Data scientists create an infrastructure through which an organization can learn from its data.

Master data management

Master data management guarantees that consistent data is accessible across the organization. It ensures accuracy, timeliness, and completeness of data and also provides context for data. Master data management creates a single and shared reference point for the entire organization, including golden records and confirmed dimensions and hierarchies.

Data analytics

Data analytics works on enterprise data (whether reference, meta, or transactional) to create insights from data. Analytics uses models created by data science teams and produces real-time reports and forward-looking models.