Ai-MicroCloud® for Financial Services

Revenue Growth, Cost Reduction, Risk Management, Compliance

Zeblok Computational's Ai-MicroCloud® is particularly well suited to Ai/ML development in financial services, providing everything your team needs to develop, train, deploy and manage Ai applications, transforming Ai/ML models into real-world business solutions. Zeblok’s Ai-MicroCloud® is an enterprise-ready turnkey Ai Platform-as-a-Service that is deployed to your existing infrastructure, including curated algorithms, accelerated data lake, seamless HPC orchestration, optimization for heterogeneous architectures, and production runtime environment, that helps data scientists and data engineers develop, customize, and rapidly deploy an Ai project as an Ai-API, generate new insights and enhance decision-making capabilities.

Zeblok can help you ingest and evaluate the value of data more efficiently, to integrate Ai:

  1. Within mission-critical processes
  2. Into your proprietary research platform, financial modeling and valuation methodology
  3. To make more informed decisions
  4. To differentiate and personalize product offerings
  5. Bring to life your most challenging data-driven strategies

How Ai/ML can help financial services companies ?

There are multiple areas ripe for data-driven initiatives within the categories of revenue growth, cost reduction, operational improvement, risk management and compliance.

Revenue Growth

Increasingly personalized service can improve customer retention, enhancing top line growth. Ai/ML models can help reduce customer churn, with product recommendations & personalization. Data-driven insights enabled by Edge inferences that use POS spending data can further enhance such capabilities.

Cost Reduction

Process improvement and automation can help decrease costs. Ai/ML models help to get faster insights into data, helping anomaly detection and automation to reduce costs.

Risk Management

Reducing risk from numerous sources is an ongoing challenge. Ai/ML models can help to accurately point to issues related to anti-money laundering ( AML ), and Know your customers ( KYC ). Purpose-built models can provide transparency in credit risk & loss forecasting, detect anomalous behavior and prevent cyber attacks.

Compliance

The financial services industry regulatory landscape is complex. Basel III for capital and liquidity, the Dodd Frank Act for derivatives, FATCA for market integrity, and DFA's Volker Rule for proprietary trading are among the list of ever-growing regulations. Ai/ML models can help financial institutions to identify those rules that apply to them and how best to structure compliance protocols.

Technical Challenges

Orchestration

According to Gartner, 85% of Ai projects fail. We believe there is a data comprehension gap. Additionally, Ai modeling environment needs to handle High-Performance Computing natively to deal with very large datasets needed for model training and optimization.

Insight Quality

Enterprises must confront the complexity of integrating multiple independent Ai software vendors (ISVs) and internally developed Ai capabilities.

Optimization

Enterprises have prevalent systems and machinery for continuous integration and delivery for applications. Perhaps they have made internal investments in MLOps or have procured a MLOps platform. Software developers.

Heterogenous Architecture

Enterprises have prevalent systems and machinery for continuous integration and delivery for applications. Perhaps they have made internal investments in MLOps or have procured a MLOps platform.

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