Kiến Thức Cơ Bản về Tự Động Hóa Tài Chính

AUTOMATE YOUR LEARNING OF AUTOMATION

Finance is evolving fast. What was done in the past by humans is nowadays almost entirely done by software.

... and, software is getting faster.

We have more advanced models, that do more things under more flexible circumstances, and that accelerate their learning.

If we don't know how to automate financial operations... we will be in trouble.

Unfortunately, courses so far don't tell you how to automate most financial operations.

They may focus on a specific area, like fraud detection, without giving you an exhaustive look at all common financial operations, and the common technologies used to automate each.

What if you could find, in one single course, all the usual automation technology, all the common use cases, and examples of how to implement them?

That is what this course aims to change.

LET ME TELL YOU... EVERYTHING

Some people - including me - love to know what they're getting in a package.

And by this, I mean, EVERYTHING that is in the package.

So, here is a list of everything that this course covers:

• You'll learn about the basics of financial automation, including the most frequently automated activities (transaction processing, financial analysis, expense management, reconciliation), the benefits that financial automation has (higher efficiency, accuracy, scalability, real-time insights, cost reduction), and the usual types of automation technology;

• You'll learn about the role of IT in finance, including the IT systems usually involved in automation (such as big ERP systems, external integrated platforms such as APIs, and internal proprietary applications), and their usual concerns (cybersecurity, compliance, disaster recovery, scalability, or model biases);

• You'll learn about human versus automated forecasting, including the usual forecasted elements (equity prices, credit scores and defaults, sales and revenue, bank customer behavior like account closures, insurance claims, and portfolio risks, for example), the characteristics of human versus automated forecasting (the statistical and numerical methods used, like time series analysis and regression, and the data usually needed for each type), the usual AI/ML forecasting models used (such as neural networks, decision trees or ensemble trees, or deep learning models), the pros and cons of each, and when to use human or automated forecasting;

• You'll learn about AI and ML as a type of automation technology, including common use cases (credit scoring, fraud detection, algorithmic trading, predictive analysis, recommendation systems), their different characteristics (explainable vs. accurate, generative vs. discriminative, supervised vs. unsupervised in training, or static vs. dynamic, for example), their major types (regression, classification and clustering), and their usual pros and cons;

• You'll learn about RPA (Robotic Process Automation) and OCR (Optical Character Recognition), used to automate repetitive tasks, including their common use cases (invoice processing, compliance and monitoring, form automation, report generation, reconciliation, expense management, etc), and their usual pros and cons;

• You'll learn about chatbots and NLP (Natural Language Processing), used to recognise and reply to text queries, including their common use cases (such as customer support and queries, transaction authorisation, finance advisory, document processing, onboarding processing, and so on), and their usual pros and cons;

• You'll learn about blockchains and smart contracts, used to fully automate transaction process in a secure manner, including their common use cases (such as automated stock trading, automated insurance claims, automated lending and escrow, or automated identity verification), and their usual pros and cons;

• You'll learn about generative AI, used to generate text, image, or other forms of media, its usual use cases (generating images, scenarios, reports, emails, documents, personalised financial advice, and more), and its usual pros and cons;

• You'll learn about key AI ethics and considerations, including AI bias (and its causes and effects), AI transparency and explainability (including the accuracy - explainability tradeoff and XAI - eXplainable AI - techniques), the dilemma of false positives versus false negatives, the role of human intervention (should it override machine decisions, and, if so, under what circumstances), and security and robustness (how to protect models against unexpected inputs, and how to not reveal too much in outputs);

• You'll learn about data considerations, including the key data disciplines (Data Management, Data Governance and Data Stewardship), important data principles (such as maintaining high Data Quality, or DQ, and seeing data as assets), and a focus on data protection by DG (such as what data to keep and when, how to protect the data, and what purposes the data can be used for);

• You'll learn about some common challenges when implementing financial automation, of three major types (technological challenges, operational challenges, and security/ethical challenges), as well as how to mitigate these;

• You'll learn about possible future trends, such as Intelligent Process Automation (IPA), quantum computing, decentralised finance, and ChatGPT and LLMs, including the challenges they may bring, and possible large-scale consequences in financial automation (the mass personalisation of everything, the interconnectedness of all systems, and a bigger focus on sustainability);

MY INVITATION TO YOU

Remember that you always have a 30-day money-back guarantee, so there is no risk for you.

Also, I suggest you make use of the free preview videos to make sure the course really is a fit. I don't want you to waste your money.

If you think this course is a fit, and can take your knowledge of dealing with change to the next level... it would be a pleasure to have you as a student.

See on the other side!