What Is New Age Asset Liability Management?

Financial institutions spend enormous resources and time on stress testing, capital planning, and risk management. The ever-changing market infrastructure and deepening regulatory and compliance initiatives have triggered disparate approaches to address each of these aspects. Unfortunately, this has often led to process duplication, multiple versions of the truth (data) and bottlenecks across different functions. In addition, there is constant pressure to increase risk appetite and provide financial instruments to empower customers to survive adverse economic conditions.

Financial institutions essentially face four types of risks – liquidity risks, interest rate risks, currency risks and capital markets risks. These risks arise mainly from an imbalance in maintaining assets and mitigating liabilities in the long term. The risks need to be managed while ensuring strategic planning and regulatory compliance. This is where asset and liability management (ALM) comes to the rescue.

The key aim of ALM is to manage the volume and timing of cash flows associated with assets and liabilities in order to increase profitability, manage risks and maintain the safety and soundness of the financial institution. In this context, risk is the gap between expected cashflows and actual cash flows. The goal is to narrow or even eliminate this gap.

Traditional vs New Age Asset and Liability Management

Traditional ALM has struggled to address the following concerns:

• The need for a flexible approach to performing in a dynamic market environment.
• Rapid model parametrisation and user-friendly interfaces to facilitate data input.
• Holistic analytics to deliver an overall view of risks and functions for better decision making.
• Ability to handle ad-hoc and what-if scenarios.

These prevent traditional ALM from becoming future-proof and evolving in-sync with market requirements. Today, financial institutions have the power of artificial intelligence and machine learning to extract business intelligence, and unify data, models and processes across organisational functions. This has created the need for the modernisation of ALM to enable an integrated, macro-level approach to maintaining a healthy balance sheet.

What Does New Age Asset Liability Management Entail

From a siloed, not too user-friendly, slow and expensive legacy systems, new age ALM allows a more unified and integrated approach to balance sheet management. Some of the benefits its offers are:

• ??Use Case Based Analytics: This has reduced turnaround time for ad-hoc and custom analytics.
• Transparency and Agility: ALM aims to improve risk management to handle interest rate fluctuations and enhance liquidity control.
• Cash Flow Modelling: Improved assessment reporting to discover typical risks associated with specific regulations for different financial instruments and jurisdictions.
• Flexibility: Advanced modelling techniques use AI and ML for flexible application of behavioural assumptions.
• Dynamic Asset Modelling: Quick identification of risks across the balance sheet over multiple horizons using granular hierarchies and adequate attribution.
• Standardisation: Seamless integration and ease of use with open-source and third-party components to model processes, data and business objects.

Modern ALM offers more than cost reduction. The current financial landscape demands a well-rounded ALM setup as a prerequisite to establishing an integrated approach to risk and capital management. It can give financial institutions a long-term competitive lead in unpredictable and highly volatile markets.

Rajesh Chauhan, Director – Products, CARE Risk Solutions adds:

“As New age ALM’s focus is to use AIML techniques while behaviouralising the customer pattern for Current Account, Saving Account withdrawal pattern, Term Deposit Rollover or Foreclosure pattern, Term Loan prepayment, Cash Credit / Overdraft rollover pattern, LC/BG invocation/devolvement pattern where various statistical techniques are used. But it is equally important that we use the good size of historical data ranging between 5-10 years otherwise behavioural results may be misleading from the factual position.”