Artificial Intelligence in Finance

The financial services industry is one of the leaders in adopting new AI and Machine learning technologies. The high volume of well organized, accurate, historical and quantitative nature of the data means the ‘raw material’ for tackling business challenges with AI is abundant and readily available. The industry is teeming with opportunities for improving operations, prediction accuracy and profitability.

Companies not already investing in AI solutions not only risk being left behind by competitors but also are likely to struggle through new upcoming rules and regulations regarding cybersecurity. AI-powered security systems will become a necessity to catch on with the exponentially growing complexity of the financial sector.

With the wealth of data available means we’ve only begun to scratch the surface and AI will continue to unlock more value for financial enterprises and their customer.

Finance

Applications of AI in the Finance Industry

Time Series Prediction
Time Series Prediction

Brainpool developed a bespoke machine learning pipeline Forstack, which stacks various models in real time to increase prediction accuracy.

Benefits:

Better prediction accuracy than single model predictions

Automated Due Diligence
Automated Due Diligence

AI can be used to review agreements, financial documents and contracts to accelerate the due diligence process in M&A and Private Equity.

Benefits:

Less possibility for human error

Faster turnaround times

Fraud Detection and Risk Management
Fraud Detection and Risk Management

Build bespoke risk models by identifying key data features and nonlinear patterns in large datasets, and early warning systems that automate reporting, portfolio monitoring and contingency plans.

Benefits:

Risk mitigation

Fraud prevention

Continuous system improvements

Portfolio Management
Portfolio Management

Machine learning algorithms calibrate investment decisions according to the investors goal and market fluctuations; with the aim of finding the best outcome against the set objectives.

Benefits:

Better insights

Smarter decisions

Increased profitability

Benefits of AI in Finance

Better and faster insights
Better and faster insights
Increased profitability
Increased profitability
Lower risk
Lower risk

AI in Finance Case Study

Case Study: ML-powered time-series prediction for FX market

Client: Japanese Tier-1 Investment Bank

The Challenge

Accurately forecasting currency movements in the short-term future and make data-driven decisions.

The Solution

Brainpool developed a Predictive Machine Learning System which used historical FX data to make currency fluctuations predictions.

Brainpool gained a thorough understanding of the client’s data structure to aggregate various data sources, which were used to uncover patterns, trends and identify key factors influencing the value of a given currency.

Results

Using large quantities of training data and making incremental changes, we were able to successfully develop and algorithm which increased the accuracy of the clients FX forecasting from 52% to 56%.

Fintech Report

AI in Action

Fintech Focus

AI in fintech has moved beyond hype. This report explores how leading companies are using it to deliver real impact - effciently, ethically, and at scale.


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