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.
As an AI model risk expert, do you want to validate and deploy robust and compliant models 10X faster?
The Causal Model Validation decision app can help with that and more. It uses Causal AI—new machine intelligence that understands true cause and effect. This powerful extensible solution helps all model risk stakeholders to:
Now model risks teams can work together to deploy models on high-impact business problems from Liquidity to Staff Attrition, reaping the PnL benefits of AI.
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.
causaLens’ approach to the banking industries biggest challenges
Objectives
To comply — as speedily and as efficiently as possible — with existing and evolving regulations for AI model risk, taking into account 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 the data science team, business units, and model risk team. High reliance on SHAP and/or Lime for explaining outcomes or limited to linear models only. Ill-prepared to adhere to the latest AI regulations: the new EU AI proposal or evolving US and Singapore regulatory guidance.
Causal AI Solution
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
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
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
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
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
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
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
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
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
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
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
Banks’ fraud defenses are continually out-maneuvered by changing attack strategies. Causal AI equips banks with an extra layer of intelligence to fight back. Adopters can expect to accelerate the development of AI systems by 10X and reduce the burden of fraud by 70
