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AI, Quantum Computing, Metaverse and Swarm Robotics for Banking

COURSE OBJECTIVE

The use of data and artificial intelligence (AI) is affecting all components of the banking ecosystem. As banks rethink how to integrate information, analyze data and use insights to improve decision-making, they will be better positioned to reduce costs, increase revenue, improve customer experiences and create new business models.

Despite the importance of artificial intelligence for risk management, operations improvement, revenue generation and customer experience improvements, the vast majority of banks are only in the formative stages of development. In fact, the prestigious consulting firm Accenture, in 2021, placed banking almost at the bottom of all industries in AI maturity.

The need for financial institutions to quickly operationalize their AI capabilities has gone from important to imperative. More than supporting risk and fraud analysis and increased productivity, a higher level of AI maturity at banks and credit unions will be a competitive differentiator, increasing business value across the organization.

Overall, the future of AI in banking looks hopeful. Banks that embrace the potential of AI in their operational processes will surely witness competitive advantages. They can make better decisions, offer personalized services, reduce costs and improve productivity. Therefore, it is essential for banks to explore and experiment with AI technologies to stay ahead in the ever-evolving digital landscape.

The objective of the course is to explain how to apply artificial intelligence to the banking sector. In particular, it explains how deep learning algorithms, machine learning, chatbots, robo-advisors, quantum computing, quantum machine learning, swarm robotics and recommendation systems add value to the banking sector.

The applications of artificial intelligence in financial risks, finance, auditing, human resources, trading, cybersecurity, financial advice, customer knowledge, anti-money laundering, among other applications, are explained.

The Metaverse is an interconnected virtual world, similar to the Internet but consisting of three-dimensional virtual spaces instead of pages on websites. It includes social networking sites and other applications that allow users to create their digital avatars and engage in real-time interactions with each other. Generation Z is familiar with augmented and virtual reality. So banks have the opportunity to create metaverse banking products aimed at young people.

The course explains the use of Metaverse, virtual and augmented reality because it offers a variety of benefits to banks and financial institutions, including better customer communication, optimized transactions, reduced costs and improved security.

Basel IV explains that quantum computers, if they reach sufficient size and power, can break the encryption schemes widely used today to ensure secure financial transactions and data. This makes quantum computing one of the most significant cybersecurity threats facing the financial system, potentially exposing all financial transactions and much of our existing stored financial data to attack.

Therefore, the course explains what quantum computing is, quantum circuits, important quantum algorithms, quantum mechanics, quantum error and correction, and quantum machine learning. How quantum computing can be implemented in a financial institution, the benefits and risks it entails.

The course addresses swarm robotics that offer banks a powerful solution to automate tasks, improve fraud detection and improve customer service. By harnessing the potential of this technology, banks can also reduce labor costs and improve customer satisfaction.

The course content is not mathematical.

WHO SHOULD ATTEND?

This program is aimed at directors, managers, consultants, regulators, auditors and those professionals who are implementing artificial intelligence in financial entities. Professionals who work in banking entities, savings banks and all those companies that wish to delve deeper into new AI technologies.

Friday, May 4, 2024
Europe: Mon-Fri, CEST 16-19 h
America: Mon-Fri, CDT 18-21 h
Asia: Mon-Fri, IST 18-21 h
Price: 6.900 €
Duration: 40 h
Presentaciones: PDF, Ejemplos en Python y R
yulia.tsedryk@fermacrisk.com
Banks
Banking Risk PDF

Agenda

Module 1: Artificial intelligence in banking

Why are banks adopting AI?

Retail banks

Non-retail banks

Applications of AI in banking

Front Office

Algorithmic trading

Financial advice

Credit Scoring

Middle office

Anti money laundering

Cybersecurity

Know your customer KYC

Back office

Operational automation

Contract analyzer

Collection information management

Benefits and opportunities

Risk and governance

Key Challenges Banks Face in Advancing AI Applications

Development problems

Technical problems

Regulatory Environment

Module 2: Banking governance and the use of artificial intelligence

Strengthen banking governance for AI adoption

Data governance framework

Machine learning and model risk management.

Cybersecurity of AI systems

Module 3: Policy responses, strategies and challenges

Monitoring Banks' AI Adoption

General principles of supervision of AI adoption by banks

Supervisors' approach to AI adoption by banks

New challenges for banking regulators in the era of AI and digitalization

Module 4: Technology in banking

Cloud-based software as a service. Cloud-based software-as-a-service (SaaS)

SSBK Self Service Banking Kiosks

SST in the banking sector: global and local contexts

Image-enabled ATMs

Digital account opening

What is digital financial inclusion like?

Interactive banking portals

Person-to-person (P2P) payments

Non-bank P2P payment methods

Bank-Centric P2P Payment Methods

Chatbots/virtual personal banker

Banking chatbot business

Video banking services

Mobile and TV banking

Safe deposit boxes with iris scanning biometrics

Physiological biometry

AI in Banking and Financial Entities
Module 5: Blockchain technology for financial markets

Key features and main applications of blockchain technology in the financial world

What is the process to add a transaction to the blockchain?

Types of blockchain

Features of blockchain technology

The power of Blockchain technology and its revolutionary applications in the financial sector

Modern banking ledger with blockchain technology

Detailed Blockchain Components Are Immutable

Blockchain data is transparent

Blockchain and banking business models

The 5 best blockchain applications in banking

Blockchain benefits for banking

Five examples of blockchain applications in banking

Inherent drawbacks of digital currencies like bitcoin

scalability

problems with cybersecurity

price fluctuation and lack of inherent value

regulations and policies

Possible drawbacks of using cryptocurrencies and DLT

Bitcoins are not accepted across the board.

Wallets can be lost

Bitcoin value fluctuates

There is no buyer protection.

Technical failures that are not known.

Deflation is built in

There is no physical form

There is no valuation guarantee

Fraud detection and claims management using blockchain management

Three blockchain features that prevent fraud

What types of fraud are carried out?

AI on the Blockchain
Module 6: Machine Learning

Artificial intelligence

Definition of Machine Learning

Machine Learning Methodology

Data Storage

Abstraction

Generalization

Assessment

Supervised Learning

Unsupervised Learning

Reinforcement Learning

Deep Learning

Typology of Machine Learning algorithms

Steps to implement an algorithm

Information collection

Exploratory Analysis

Model training

Model Evaluation

Model improvements

Machine Learning in consumer credit risk

Programming languages: Python, R, C++, etc.

Analysis of main tools: Microsoft Azure, Tensorflow, Matlab, Caffe, H2O, Knime, IBM SPSS Modelller, etc.

Module 7: Introduction to Deep Learning

Definition and concept of deep learning

Why the use of deep learning now?

Artificial neural networks

Neural network architectures

Activation function

cost function

Gradient Descent Optimization

Hyperparameters

Feedforward network

Convolutional neural networks

Use of deep learning in image classification

Use of deep learning in banking

Recurrent Neural Networks

Generative Adversarial Networks (GANs)

Growth of GANs

Machine Learning and Deep Learning
Module 8: Deep Learning and Machine Learning in Banking

Graph neural networks for inverter network analysis.

Using ML to predict credit card customer defaults

Application of deep learning methods for econometrics.

Application of AI in finance

AI and ML techniques for simulation of markets, economics and other financial systems

Infrastructure to support AI and ML research in finance

Chatbots and advisory robots for payments and innovation

AI/ML-based evaluation models

Validation and calibration of multi-agent systems in finance

Advancement of the ML for financial stability

AI-based blockchain in financial networks

Technology Challenge: Deep Learning Considered Too Resource-Intensive

Credit scoring models that use ML algorithms

Module 9: Unsupervised ML for Banking Services

Supervised learning for the prevention of money laundering, document analysis and loan underwriting, commercial agreements and high-frequency operations.

Fraud Detection

Customer experience and segmentation.

Admission and credit rating

Difficulties in industry adoption

Module 10: Robo-Advisors in banking

Robo-advisors is a supervised learning tool to optimize portfolios

What is a Robo-advisor?

Understanding Robo-advisors

Portfolio rebalancing

Fundamental advantages of Robo-advisors

Hire a Robo-advisor

Robo-advisors and regulation

How Robo-advisors make money

Best-in-class Robo-advisors

Module 11: Natural language processing and applications in finance

Financial technology and natural language processing.

NLP-based finance

NLP-based investment management

NLP-based know-your-customer approach

Applications or systems for FinTech with NLP methods

Crowdfunding analysis with text data.

Text-oriented customer preference analysis

Insurance application with textual information.

Telematics: car and health insurance

Telematics and car insurance

Benefits of Telematics-Based Auto Insurance

Text-based market provisioning

Module 12: Chatbots in Banking

Extract information from conversations

Chatbot as a search engine

Natural language understanding

Natural language generation

Building a system

Chatbots in banking

Offer personalized customer service.

Manage transaction processing

Send reminders

Up-Sell Cross-Sell

Deep Learning and Machine Learning in Banking
Module 14: Metaverse in Banking

Role of AI in the Metaverse

AI FOR THE METAVERSE: TECHNICAL ASPECT

Three-dimensional (3D) technologies

VR virtual reality

Digital Banking

Virtual Banking

VR virtual reality as a determinant of growth

Virtual reality as an innovative tool on the sequence of applications.

Application of virtual reality in banking: overview of current trends

AR Augmented Reality

Data visualization

Customer service

Bank account management

Virtual commerce

Asset Security

Communication

Extended reality: XR

Mixed Reality (MR)

Machine vision

computer vision

block chain

Networks

Digital twins

Neural interface

NFT, 5G and Web 3.0

Artificial Intelligence for the Metaverse
Module 15: Artificial intelligence AI for Cybersecurity in Banking

Artificial intelligence AI for cybersecurity

Cybersecurity Anomaly Detection

Advanced deep learning models

Use of Supervised Learning in cybersecurity

Use of Unsupervised Learning in cybersecurity

Phishing detection and mitigation

SVM

Clustering

Deep Learning for attack and malware detection

Recurrent Neural Networks

Intrusion detection

Network Traffic Analysis

Botnet detection

Machine learning to detect DDoS attacks

Fraud detection in financial transactions

Detection in sensors

Banking fraud analysis

Advanced machine learning techniques for cybersecurity

Main AI vendors for cybersecurity

Visualization tools

Benefits of AI in cybersecurity

Growth of AI in cybersecurity

Challenges and limitations

Module 16: Cybersecurity and Advanced AI

Suspicious activity detection

Machine learning algorithms for intrusion detection Malware detection using transformers and BERT

Detection of fake reviews

Machine-generated text detection

Fake news detection with graph neural networks

Attack Models with Adversarial Machine Learning

Developing robustness against adversarial attacks

Adversarial machine learning

Adverse attacks for cybersecurity

Typology of adverse attacks

Adversarial samples

Generative Adversarial Network (GAN)

Fast Gradient Sign Method (FGSM)

Creating adversarial malware samples using GAN

Protecting user privacy with machine learning

Cybersecurity and AI
Module 17: Quantum Computing and algorithms

Objective: Quantum computing applies quantum mechanical phenomena. At small scales, physical matter exhibits both particle and wave properties, and quantum computing takes advantage of this behavior using specialized hardware. The basic unit of information in quantum computing is the qubit, similar to the bit in traditional digital electronics. Unlike a classical bit, a qubit can exist in a superposition of its two "ground" states, meaning that it is in both states simultaneously.

Future of quantum computing in insurance

Is it necessary to know quantum mechanics?

QIS Hardware and Applications

Quantum operations

Qubit Representation

Measurement

Overlap

Matrix multiplication

Qubit Operations

Multiple Quantum Circuits

Entanglement

Deutsch algorithm

Quantum Fourier transform and search algorithms

Hybrid quantum-classical algorithms

Quantum annealing, simulation and optimization algorithms

Quantum machine learning algorithms

Module 18: Introduction to quantum mechanics

Quantum mechanics theory

The wave function

Schrödinger's equation

The statistical interpretation

Probability

Standardization

Impulse

The uncertainty principle

Mathematical Tools of Quantum Mechanics

Hilbert space and wave functions

linear vector space

Hilbert space

Dimension and bases of a Vector Space

Integrable square functions: wave functions

Dirac notation

Operators

General definitions

Hermitian deputy

Projection operators

Commutator algebra

Uncertainty relationship between two operators

Operator Functions

Inverse and Unitary Operators

Eigenvalues and Eigenvectors of an operator

Infinitesimal and finite unitary transformations

Matrices and Wave Mechanics

Die mechanics

Wave Mechanics

Quantum Computing
Module 19: Quantum communications

Theoretical Information Security

RSA-129

Grover's algorithm

Shor's algorithm

Fourier Transform algorithm

Quantum Key Exchange

Quantum networking

SIGINT and encryption adoption

Secret sharing

Quantum random number generation (QRNG)

The NIST Randomness Beacon

Quantum key distribution

BB84

How QKD works

Why QKDI is safe

Quantum Money

Quantum Computing and Bitcoin

QKD gains momentum

QKD Commercialized, Miniaturized

Quantum internet

Module 20: Quantum implementation in financial entities

Derivatives prices

Portfolio management using traditional machine learning algorithms.

Quantum algorithm portfolio management implementation.

Implementation of classical and quantum machine learning algorithms for a credit scoring scenario.

Deployment in quantum clouds

NISQ Quantum Hardware Roadmap

The Quantum Workforce Barrier

Key Skills for Training Resources

Infrastructure integration barrier

Identifying the potential for advantage with QML

Financing or budget problems

Market maturity, hype and skepticism

Roadmap for early adoption of quantum computing by financial institutions

Training of quantum managers

Quantum Communication and Implementation
Module 21: Quantum proof of the financial system

The quantum cyber threat to central bank IT systems

Why quantum computing represents a cyber threat

The potential threat to current cryptographic techniques

How to defend against the quantum threat

An international cooperation organized by NIST

Solutions can be implemented now

How to prepare and create quantum safe environments

Post-quantum cryptography vs quantum cryptography

Central banks must prepare now

Project Leap

Objectives and scope

Solution designs

Implementation and testing

Findings

Crypto Agility

Performance

Security

Conclusion and next steps

Need for a migration plan

Implementation challenges

Next steps

Module 22: Post-quantum digital signatures and Lattice Based Cryptography

Introduction

Digital signatures and their security

Secure signatures in ROM

Quantum adversary modeling

NIST PQC Standardization Competition

Lattice Based Cryptography

Introduction to Ring-LWE

Discrete Gaussian Sampling

NTRUSign

GPV Framework

Fiat-Shamir with cancellations

CRISTALES-DILITHIUM signature scheme

MQ-based signatures

Hard problems based on MQ

Oil and vinegar signatures

HFE Signatures

Signatures based on symmetric key techniques

Signatures based on supersingular isogenies

Quantum Risk in Banking
Module 23: Cloud computing, security risks and AI

Models Overview

Security Risk Assessment Frameworks

Cloud Risk Assessment Models

Cloud Adoption Risk Assessment Model

Advisory, objective and bifunctional risk analysis

Cloud Risk Assessment Frameworks

Cloud Security Risk Management Framework

Information Security Risk Management Framework

Security Risk Assessment Framework

Performance analysis of existing models and frameworks

The role of AI in Cloud Computing

AI applications in Cloud Computing

Advantages of implementing AI in cloud environments

Cloud Computing AI
Module 24: Perspectives and challenges of using AI in the audit process

Background and relevant aspects of the audit

Artificial intelligence in audit

Use of audit expert systems

Use of neural networks in audit

Framework for including AI in audit

Components

AI strategy

Governance

Human factor

Cyber resilience

AI competencies

AI in Data Quality

Data architecture and infrastructure

Performance measurement

Black Box Treatment

Transformation of the audit process

Impact of digitalization on audit quality

Impact of digitalization on audit companies

Steps to transform manual audit operations to AI-based

Applications of artificial intelligence in auditing: some examples

KPMG

Deloitte

PwC

Ernst & Young (EY)

General features

Specific aspects of the audit company

Business organization aspects

AI in Audit Processes
Module 25: AI in hiring human resources HR

Development of theories and hypotheses

Technology anxiety

Performance expectations

Effort Expectation

Social influence

Resistance to change

Facilitate conditions for staff

Behavioral intention to use and actual use

Moderating effects of age status

Design of the investigation

Survey design

Procedure for collecting data and information from participants

Measuring tools

Results and hypothesis tests

Analytic technique

Measurement model evaluation

Evaluation of structural models

Tests of direct effects

Tests for moderating effects

AI in Human Resources Processes
Module 26: Quantum computing: the future of artificial intelligence and its applications

The steps required to create an effective model for unsupervised tasks

Critical steps to enable further development of quantum AI

How quantum computing can benefit artificial intelligence

Handling large data sets

Solve complex problems

Speed

Building better models

Integration of multiple data sets

Technical obstacles

Application of quantum computing

Artificial intelligence and ML

Cybersecurity and cryptography

Finance

A leap between ML and DL quantum models

Quantum error correction

Short-term quantum devices

Variational circuits

Quantum computing gates

Performance evaluation of quantum systems

Measurement optimization.

Quantum ML for pattern classification

Quantum Technology Laboratory QTL

Explaining the equations of the QTL approach

AI Quantum
Module 27: Introduction of Swarm Intelligence

Introduction to swarm behavior

Individual versus collective behaviors

Swarm intelligence concepts

Particle Swarm Optimization (PSO)

Main concept of PSO

Types of communication between swarm agents

Examples of swarm intelligence

History of swarm intelligence

Taxonomy of swarm intelligence

Properties of swarm intelligence

Swarm behavior models

Self-propelled particles

Design Patterns in Cyborg Swarm

Creating design patterns

Design pattern primitives and their representation

Updating design patterns in Cyborg

Behaviors and data structures

Cyborg Swarm Basics

Information exchange in the workplace

Clearinghouse

Cyborg Job Features

Cyborg's Greater Usefulness

Get an extra reward

Cyborg Design Property

Expanding Cyborg's design

Storing information in Cyborg

Information exchange at any time

New design pattern rules in Cyborg

Cyborg inspired by bees

Swarm Robotics in Banking
Module 28: Swarm and evolutionary intelligence in deep and quantum learning

LSTM and Bi-LSTM deep networks

Deep CNN (Convolutional Neural Networks)

CNN and LSTM optimization

Topology optimization

Weight optimization

experiments

BI-LSTM Optimization

Data set

objective function

Experimental setup

Genetic algorithm parameters.

w-PSO Adaptive Parameters

Two-way LSTM training parameters

CNN Optimization

CNN – PSO Model

Evaluation Metrics

Covid-19 Chest X-ray Dataset

Experimental results

CNN without PSO

PSO Optimized CNN

Particle Swarm Optimization: Classical and Quantum Perspectives

Quantum PSO

QBSO algorithm

An empirical analysis of swarm intelligence techniques on ATM cash withdrawal forecasting

Swarm Robotics for financial and banking services

The benefits of using Swarm Robotics for financial risk management

Quantum Swarm Robotics and Deep Learning
Module 29: Recommendation systems for banking

Introduction of recommender systems

Types of recommendation systems for banking and financial services

Collaborative filtering

Content-based filtering

How recommendation systems work

Benefits of recommendation systems in financial services

Better personalized services for customers

Increased sales and revenue for financial institutions

Improved risk management and fraud prevention

Implementation of recommendation systems

Recommendation Systems