Competing in financial services requires trustworthy technology. However, correlation-based machine learning techniques lack reliability, flexibility, and transparency. Leading wealth managers and market-makers trust the power of Causal AI in capital markets to augment their client analytics, market analysis, and decision-making.
Servicing clients is at the core of what makes banks succeed. From institutional clients to personal wealth management advisory, 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 those relationships.
With Causal AI, wealth managers can identify the causal drivers behind their clients’ behavior, resulting in informed decisions, improved client loyalty, and growing assets under management.
Banks need to demonstrate that they have strong model validation in place to justify the use of complex algorithms by both internal auditors and external regulators.
With Causal AI, banks can automatically infer the causal drivers that led to model predictions and work with compliance managers to develop the most cost-effective solutions to make sure that these predictions are fair and in line with expectations.
causaLens co-founders Darko Matovski and Maxim Sipos discuss why current AI fails and how Causal AI unlocks superior decision-making, in our Capital Markets webinar. Applied Data Science Director Andre Franca presents some causaLens success stories.
causaLens’ approach to the financial services industry biggest challenges:
Objectives
To get relevant client segmentation and next best action for either institutional or individual clients with each suggestion being explainable, transparent and justifiable.
Current Approach and Pain Points
Based on basic filters without any predictive power.
Causal AI Solution
Objectives
causaLens empowers exchanges, data sourcing teams & data vendors to autonomously discover value in time-series data.
Current Approach and Pain Points
Standard AI techniques are confused by large amounts of intercorrelated and mismatched data.
Causal AI Solution
Only Causal AI allows users to extract valuable signals and build exciting data products that are easily monetized.
Objectives
Track causal relationships between ESG pillars and financial performance. Ensure compliance with current and future regulatory requirements utilizing 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
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