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AI models company of Datrix group

AI Model Serving to reshape Banking & Finance Industries

Automatize banking & financial services and processes, boosting efficiency/profitability and reducing costs.

AI is revolutionizing the banking & financial industries

In order to survive in the 21st century, Banks and Financial institutions must quickly set up intelligent systems for collecting and using different kinds of data.

$1.2 Trillions

Finance industry investments in AI by 2035.

23,17%

Market growth for artificial intelligence in financial technology between 2020 to 2025.

+80%

Worldwide Banks are highly aware of the potential benefits presented by AI.

$447 Billions

the estimated aggregate potential cost savings for financial organizations from AI by 2023.

The banking & finance industry faces constant challenges in applying AI

1

Making AI systems responsible and trustworthy

The lack of trusted, good-quality information and specific security measures to manage it can generate privacy threats.

2

Making AI systems explainable and valuable

To remain compliant, minimize the risk of making bad business decisions and prove ROI.

3

Managing the convergence of AI with other technologies

The data center technologies used by financial organizations (virtual machines, shared drives, etc.) do not perform well on fast data / big data.

4

Training current employees to work with AI systems

That includes risk and regulatory teams, who often lack the needed skills to assess AI models’ impact.

5

AI creating new cyber threats

Data poisoning pollutes the training data to mislead the machine learning algorithm and render the output either obfuscatory or harmful.

Applications

Innolva

Application:Corporate Lending – Credit Scoring / Risk Underwriting (Back Office)

Data Source: company balance sheet, web search, news coverage, job posting, social buzz etc

KPI: Time & cost saving

WHAT?
AI model that provides a richer, individualized SMEs credit score assessments based on an array of additional real-time digital factors that, combined with traditional financial information, can predict firms’ default with a higher accuracy.

WHY?
Minimize risk exposure. Greater and faster access to credit. Higher knowledge of the borrower.

HOW?
Combining traditional financial information based on the past performance of the borrower with real time digital indicators such as digital presence, digital popularity, digital reputation, employment opportunities, etc. Our model explore more than 1.400 variables using:

  • Machine-Learning
  • Deep-Learning
  • Parallel computing

Banca Sella

Application: Personal Finance / Retail Banking – KYC Due Diligence Automation (Middle Office)

Data Source: ID cards, payroll documents, insurance policy documents, etc.

KPI: Operation Efficiency

WHAT?
AI model that automates detection of errors, discrepancies and anomalies to determine the validity of identity/personal documents in the credit evaluation process.

WHY?
Reduce time spent sourcing data and documents. Cost saving and greater control over KYC

HOW?
Harnessing the most advanced GDPR-compliant AI technologies to convert content on physical documents into digital/editable form. In particular:

  • OCR (optical character recognition)
  • Computer Vision
  • Object Detection
  • Machine-Learning
  • Deep-Learning
  • Parallel computing

Compass

Application: Retail Lending  – Tailored Personalized Products (Front Office)

Data Source: CRM, Web/Mobile Analytics

KPI: Life-Time Value, Retention Rate

WHAT?
AI model that automates the creation and prioritization of customer clusters that shows similar behaviors, risk profiles, characteristics and shopping habits.

WHY?

Maximize customers retention providing each cluster with the best product/offer by taking into consideration their preferences, risk attitude, prior purchases and interactions.

HOW?
Harnessing the most advanced GDPR-compliant AI technologies to match socio-demographic data, navigation behavior data and shopping behavior data to generate accurate and actionable customer clusters for business growth. In particular:

  • Time-Series Analysis
  • Machine-Learning
  • Parallel computing

Other possible applications

Asset Performance Optimization

Identify which factors matter to improve products by analyzing product performance data

Customer Churn Prevention

Apply deep neural network techniques to predict credit card defaults; identify up/cross-selling opportunities and score leads for maximum conversion rates

Fraud detection and prevention

Prevent automated attacks by detecting and protecting against malicious web behavior

Contract Life-Cycle Management

Analyze custom contracts, policies or other legal documents to speed up processing and reduce errors

Next-Gen Due Diligence Process

Fasten & simplify M&A due diligence process virtualizing enormous volumes of paper documents and applying advanced analytics

Security and Risk Management

Evaluate the activity of users and other entities to discover anomalies that could indicate external security infractions

AI Model Services can impact on different banking and financial market activities

Cybersecurity is in our DNA

Cs-Aware and Cs-Aware Next are 2 EU-funded projects where tech-companies, universities and research centers work together to take a big step towards the automation of cyber incident detection, classification and visualization for public and private organizations.

In particular 3rdPlace is the «tech engine» of the consortium enabling:

  • Data collection from both internal sources (eg. Server logs) and external (vertical sites, social media, supply chain database, etc.)
  • Advanced Data Analysis through AI in order to detect, classify and visualize cybersecurity incidents in real-time, supporting the prevention or mitigation of cyber attacks.

Our partners

Contact us to learn more about our Machine Learning models

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