PaymentsJournal
No Result
View All Result
SIGN UP
  • Commercial
  • Credit
  • Debit
  • Digital Assets & Crypto
  • Digital Banking
  • Emerging Payments
  • Fraud & Security
  • Merchant
  • Prepaid
PaymentsJournal
  • Commercial
  • Credit
  • Debit
  • Digital Assets & Crypto
  • Digital Banking
  • Emerging Payments
  • Fraud & Security
  • Merchant
  • Prepaid
No Result
View All Result
PaymentsJournal
No Result
View All Result

How AI Is Reshaping Risk Management in Corporate Banking

By Stuart Tarmy
March 22, 2022
in Artificial Intelligence, Commercial Payments, Corporate Banking, Emerging Payments, Featured Content, Industry Opinions
0
0
SHARES
0
VIEWS
Share on FacebookShare on TwitterShare on LinkedIn
How AI Is Reshaping Risk Management in Corporate Banking

How AI Is Reshaping Risk Management in Corporate Banking

In corporate banking, risk management strives to limit the risk exposure and asset losses for a financial institution. It can be extremely complicated, and it requires sophisticated data analytics that is increasingly real time. Its scope is very wide, and it extends throughout all of the bank’s different businesses. Key risk management areas of interest include (and this is not exhaustive) fraud, investment, trading, margin and derivatives exposure, payment risk, credit exposure, debt levels and liquidity to meet day-to-day and ongoing obligations, regulatory compliance, and financial market exposure (e.g., investments, foreign exchange exposure).

When risk management falls short, it can lead to billions of dollars in losses and reputational damage. As risk can happen across many departments, it’s difficult for auditors and risk managers to catch problems early without proper controls and stress testing.

For example, a federal judge last year ruled that Citigroup is not entitled to recoup $893 million it accidentally wired to Revlon, saying it was “a banking error of perhaps unprecedented nature and magnitude.” It was another blow to Citigroup, which received a $400 million fine in 2020 for “longstanding failure to establish effective risk management.”

In another well-known example, the failure of Archegos Capital Management last year led to more than $10 billion in losses, including $5.5 billion in losses for Credit Suisse and a nearly $3 billion loss for Japanese bank Nomura Holdings. Last December, the Federal Reserve Board provided additional guidance to banks of its expectations regarding risk management practices in investment banking.

These types of financial losses highlight the need for improved corporate bank risk management, especially in the face of increasing competitive pressures and regulatory oversight.

Using AI to Extract Valuable Insights in Risk Management

To manage risks in real time and make intelligent decisions, financial institutions over the next decade will continue to prioritize advanced analytics by using artificial intelligence (AI) systems to extract deeper insights. The most advanced banks are starting to utilize neural nets and deep learning, which can ingest millions of data points in milliseconds to detect problems. According to McKinsey’s research, the percentage of a corporate bank’s risk management staff focused on analytics will increase from 15% to 40% by 2025.

Corporate banks can use AI to determine high-risk areas and provide automation and controls to limit the risk. AI can identify patterns and predict outcomes to help banks understand and mitigate risk more effectively. AI can help corporate banks strategize for the future, make precise real-time decisions, improve risk modeling, provide better monitoring, and minimize costly human errors.

To accomplish this, there are three key requirements AI systems need for data scientists to select, tune, and build the best algorithms. First, they need to use massive volumes of data to learn and then improve and optimize information for an organization. Second, AI systems need to consume multiple data sources, such as transactional, account, customer, payments, and various third-party data, often at the edge or from different data silos or geographies. Third, AI systems need a hyper-capable database that can ingest and process all this data fast, as in milliseconds, to make decisions in real time.

Many banks still use traditional data platforms with inconsistent and incomplete datasets from disparate sources that are hard to extract and act in batch mode. For banks that require a more capable, real-time approach, a modern database engine is needed.

For example, a leading multinational financial services company moved to a modern data platform to accurately manage in real time account authentication, trade authorization, and compliance/risk controls. The data platform handles large amounts of data quickly, ensuring that the company provides best-in-class responsiveness to customers’ trading activities while remaining in compliance with securities regulations and internal controls. At the same time, it ensures consistent data and performance with scalability and low latency, even during peak trading periods.

Financial institutions are susceptible to risk due to the sensitive information they collect. Advanced analytics and automation are reshaping the way risk is managed, and it’s no surprise that the leading firms are moving to sophisticated AI-based solutions. With more corporate banks facing unprecedented worldwide regulatory and market pressures, relying on AI will help automate processes to minimize costly human errors and provide greater visibility and insight into the critical risk categories. To meet these goals, a modern, real-time data platform that can ingest, process, and deliver sophisticated data analytics quickly, reliably, and consistently is critical.

0
SHARES
0
VIEWS
Share on FacebookShare on TwitterShare on LinkedIn
Tags: AIAnalyticsArtificial IntelligenceAutomationCorporate BankingDataData AnalyticsReal TimeRiskRisk Management

    Get the Latest News and Insights Delivered Daily

    Subscribe to the PaymentsJournal Newsletter for exclusive insight and data from Javelin Strategy & Research analysts and industry professionals.

    Must Reads

    Making Real-Time Payments a Reality

    Fulfilling the Promise: Making Real-Time Payments a Reality

    July 10, 2025
    mortgage

    The Rich Benefits of In-House Payment Systems

    July 9, 2025
    digital cards

    Beyond Plastic: Why Digital Cards Are the Future

    July 8, 2025
    What Premium Card Overhauls by Chase and Amex Reveal About the Credit Card Market

    What Premium Card Overhauls by Chase and Amex Reveal About the Credit Card Market

    July 7, 2025
    Rewire Acquires Imagen, Looking at Prepaid Cards for Migrant Workers

    Smells Like Team Spirit: What Makes Cobranded Credit Cards Work

    July 3, 2025
    uk banking outages

    New Continuous Strategies for Battling Account Takeovers

    July 2, 2025
    Fraud Monitoring

    What to Expect When Nacha’s Fraud Monitoring Rules Take Effect

    July 1, 2025
    payments

    Don’t Just React to What’s Next in Payments—Anticipate It

    June 30, 2025

    Linkedin-in X-twitter
    • Commercial
    • Credit
    • Debit
    • Digital Assets & Crypto
    • Digital Banking
    • Commercial
    • Credit
    • Debit
    • Digital Assets & Crypto
    • Digital Banking
    • Emerging Payments
    • Fraud & Security
    • Merchant
    • Prepaid
    • Emerging Payments
    • Fraud & Security
    • Merchant
    • Prepaid
    • About Us
    • Advertise With Us
    • Sign Up for Our Newsletter
    • About Us
    • Advertise With Us
    • Sign Up for Our Newsletter

    ©2024 PaymentsJournal.com |  Terms of Use | Privacy Policy

    • Commercial Payments
    • Credit
    • Debit
    • Digital Assets & Crypto
    • Emerging Payments
    • Fraud & Security
    • Merchant
    • Prepaid
    No Result
    View All Result