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Causal AI in Banking

Banks are adopting AI to solve their most challenging problems — from risk management to marketing optimization. However, they are disappointed with the failures of correlation-based machine learning technology, which is often confined to automating back-office administrative tasks. Using Causal AI in banking inspires the trust needed to unlock the true value of AI in banking.

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Top of mind for our customers in banking

  1. Competition from fintechs and new entrants
  2. Risk management under extreme uncertainty
  3. Increasing regulatory pressures
  4. AI systems that pose risks, lack transparency, and break during crises

Solutions

Servicing clients is at the core of what makes banks succeed. From retail to personal wealth management, having a data-driven understanding of what customers want and how they respond to changes is the key to strengthening that relationship. Causal AI identifies what really makes your customers tick, and recommends the most cost-efficient actions to improve client relationships. 

Causal AI empowers risk managers with a new class of models that uncover deeper structures within the data. Credit analysts can have their domain expertise augmented by Causal AI, which can judge a counterparty’s credit risk. Liquidity outflows can be modelled using causal methods, disregarding spurious correlations in data, and producing more generalizable models. Causal AI helps risk managers produce a more holistic view of current market movements beyond correlation analysis, identifying regime changes and understanding how the market behaves in each regime.

Compliance and operational risk events have become a significant source of losses for large banks today. With Causal AI, banks can identify and mitigate these risk events. Our technology automatically infers the causal drivers that led to these errors, and works with compliance managers to develop the most cost-effective solutions to prevent these losses in the future.

Forecasting lending losses and setting credit limits is a data-intensive problem that is riddled with confounders and spurious correlations. Current machine learning techniques produce models that fail whenever there are regime changes, do not generalize well to new settings, and may use proxy variables that are discriminatory in nature. Causal AI makes far more trustworthy lending recommendations with better outcomes. 

Trusted leader in the Industry

Banking Pain Points

causaLens’ approach to the banking industries biggest challenges

CRM — Personalized products and pricing

Objectives
To create market-leading products that attract new customers, help retain existing customers and generate new revenue streams and additional profit

Current Approach and Pain Points
Decision-making process based on limited or variable data and heuristics; unreliable forward planning.

Causal AI Solution

  • Optimized pricing — which also takes account of market conditions including competitive pressures — maximizes ROI and PnL
  • Stress test launch product
  • Envisage future scenarios to inform forward product planning

CRM — Customer balance sheet forecasting

Objectives
To use forecasting to: i) shape and tailor products ii) maximize customer satisfaction and value iii) enhance liquidity management and iv) more accurately assess and manage client attrition costs.

Current Approach and Pain Points

Although they can provide limited projections, spreadsheet models are fragile and as such, unreliable: they cannot factor in unpredictable, non-data related developments or recommend a course of action.

Causal AI Solution

  • Cause and effect time series forecasts supported with recommendations on how to optimize customer management and balances
  • Tailored customer alerts help Relationship Managers optimize customer value and PnL

CRM — Capital Markets and Wealth Management

Objectives

To issue timely updates based on projected price moves and/or market sentiment which may have a positive or negative impact on their investors’ portfolios.

Current Approach and Pain Points

General overviews, impersonal, lacking relevancy.

Causal AI Solution

  • Highly personalized and directly relevant updates based on dynamic market movement models
  • Tailored updates increase PnL by improving customer engagement, building loyalty and encouraging additional product uptake

CRM — Marketing Attribution and Channel Strategy

Objectives

To refine strategies and spend by understanding which channels and content are most effective in terms of customer acquisition, sales and ROI.

Current Approach and Pain Points

Inaccurate and purely correlation-based models, spreadsheets, business intelligence and no mapping of customer journey to value.

Causal AI Solution

  • Assign specific values to individual channels and content, simulate future prospect and conversion scenarios and optimize marketing budget for maximum ROI

Credit Risk and Early Warning Models

Objectives

To develop a credit risk model that maximizes PnL by promoting fair and unbiased lending, helps vulnerable customers avoid problems and is capable of responding to changing economic environments.

Current Approach and Pain Points

Simple linear models founded on limited behavioral and alternative data are unreliable in volatile market conditions. Current approaches create issues relating to bias and fairness.

Causal AI Solution

  • Models that can justify and explain decisions and incorporate real-life continuous risk assessment processes increase PnL and robustness to shocks
  • Bias and fairness sensitivity and counterfactuals built in
  • Feed off causaLake to identify external signals that influence credit risk over time

Recovery Management and Collections

Objectives

To avoid defaults by optimizing the in-life customer management process. As and when defaults occur, to ensure debts are managed so as to minimize losses while protecting vulnerable customers.

Current Approach and Pain Points

Escalation procedures, which are not tailored to the borrower, often involve expensive collection agencies, financial loss and dissatisfied customers. Regulatory constraints are difficult to model.

Causal AI Solution

  • Creates a customer-centric escalation and a default risk and collections strategy
  • Optimizes response based on the most effective channels and messaging and strikes a balance between risk and value while complying with regulations
  • Reduced costs and improved PnL

Financial Crime, Fraud, AML and Market Abuse

Objectives

To mitigate the costs and the regulatory and reputational risks caused by online fraud, scams, money laundering and market abuse.

Current Approach and Pain Points

The rules-based approach, coupled with limited machine learning models spread across multiple point solutions and enterprise systems, make it difficult to keep up with evolving fraud attacks and modus operandi. Modifications can take up to nine months to complete and new attack vectors in social engineering scams and associated regulations, may cause acute problems.

Causal AI Solution

  • Holistically models the attack vectors from domain knowledge to data
  • Provides globally explainable models with simulation and intervention analysis for operational scenario modeling and strategy optimization
  • A model that encompasses sub models, point solutions, raw data and domain knowledge in an over-arching, decision-making system to nullify different types of attacks
  • Increases agility by 10x and reduces fraud by a further 70%

Liquidity, Treasury, Asset and Liability Management

Objectives

To improve liquidity, asset and liability management by establishing the correct balance between risk, regulations and profit opportunities.

Current Approach and Pain Points

Spreadsheets and heuristic models based on risk appetite for liquidity coverage ratio and net stable funding ratio, portfolio level aggregations and simple intraday liquidity management. Cash may not be utilised as efficiently as it could be and risks may be heightened.

Causal AI Solution

  • Builds liquidity, asset and liability forecasting and alerting models based on internal and external macro-economic data
  • Develops finely detailed models from customer cohort to portfolio level
  • Provides accurate forecasting of operational balances, price sensitivity and volatility for risk management and optimizes and extends asset and liability management
  • Enhances PnL

Stress Testing and Risk Management

Objectives
To ensure compliance with current and future regulatory requirements utilising accurate scenario planning, stress testing and reverse stress testing.

Current Approach and Pain Points

Linear models that draw on small data sets, offer limited and/or inflexible scenarios and involve a lengthy implementation process.

Causal AI Solution

  • Accurate and explainable models that enable domain input from the start
  • Identifies and models non-linear causal relationships
  • Accommodates scenarios of any and all types and reverse stress testing
  • Model development to implementation 30x faster

Budget Models and PnL Forecasting

Objectives
To comprehend the relationship between budgets, risk mitigation, results, compliance and PnL and utilise funds to maximum advantage.

Current Approach and Pain Points

Spreadsheet models and heuristics underpinned with limited statistical forecasting.

Causal AI Solution

  • Models that see the business as a dynamic system of moving and interacting parts and take account of the external environment
  • Provides more accurate forecasts, scenario planning and optimization of budget allocation strategies
  • Heightens visibility and understanding of budgets and ROI
  • Substantiates intervention and strategy decisions, increases PnL and minimizing risk

Enterprise Resource Management

Objectives
To improve business performance and efficiency by understanding how allocations, constraints and supply and demand have on resource management.

Current Approach and Pain Points
ERP systems and reporting using rules-based spreadsheet models and some statistical forecasting.

Causal AI Solution

  • Models that see the business as a dynamic system of moving and interacting parts and take account of the external environment
  • Provides more accurate forecasts, scenario planning and optimization of budget allocation strategies
  • Heightens visibility and understanding of budgets and ROI
  • Substantiates intervention and strategy decisions, increases PnL and minimizing risk

Model Risk Management, Governance and Regulation

Objectives
To comply — as speedily and as efficiently as possible — with existing and evolving regulations (including the new EU AI regulations) for model risk and AI, taking account of the increased emphasis on explainability, stability, fairness, bias and trust.

Current Approach and Pain Points
Control and process framework involving manual reviews and expensive iteration process between data science team, business units and model risk team. A high reliance on SHAP and/or Lime for explaining outcomes or limited to linear models only. Ill-prepared for either the new EU AI requirements or evolving US and Singapore regulatory specifications.

Causal AI Solution

  • CausalOps model risk framework — risk assess and evaluate existing models, testing features, causality, explainability, bias, fairness, sensitivity and robustness to change
  • A user interface that accelerates model review and acceptance by allowing business, DS and model review teams to collaborate and test iterations in one session
  • Accelerate end to end model adoption speed by 30X