What Is Artificial Intelligence (AI)?

Artificial intelligence (AI) refers to the simulation of human intelligence by software-coded heuristics. Nowadays this code is prevalent in everything from cloud-based, enterprise applications to consumer apps and even embedded firmware.

The year 2022 brought AI into the mainstream through widespread familiarity with applications of Generative Pre-Training Transformer. The most popular application is OpenAI’s ChatGPT. The widespread fascination with ChatGPT made it synonymous with AI in the minds of most consumers. However, it represents only a small portion of the ways that AI technology is being used today.

The ideal characteristic of artificial intelligence is its ability to rationalize and take actions that have the best chance of achieving a specific goal. A subset of artificial intelligence is machine learning (ML), which refers to the concept that computer programs can automatically learn from and adapt to new data without being assisted by humans. Deep learning techniques enable this automatic learning through the absorption of huge amounts of unstructured data such as text, images, or video.

Understanding Artificial Intelligence (AI)

When most people hear the term artificial intelligence, the first thing they usually think of is robots. That’s because big-budget films and novels weave stories about human-like machines that wreak havoc on Earth. But nothing could be further from the truth.

Artificial intelligence is based on the principle that human intelligence can be defined in a way that a machine can easily mimic it and execute tasks, from the most simple to those that are even more complex. The goals of artificial intelligence include mimicking human cognitive activity. Researchers and developers in the field are making surprisingly rapid strides in mimicking activities such as learning, reasoning, and perception, to the extent that these can be concretely defined. Some believe that innovators may soon be able to develop systems that exceed the capacity of humans to learn or reason out any subject. But others remain skeptical because all cognitive activity is laced with value judgments that are subject to human experience.

As technology advances, previous benchmarks that defined artificial intelligence become outdated. For example, machines that calculate basic functions or recognize text through optical character recognition are no longer considered to embody artificial intelligence, since this function is now taken for granted as an inherent computer function.

AI is continuously evolving to benefit many different industries. Machines are wired using a cross-disciplinary approach based on mathematics, computer science, linguistics, psychology, and more.

Applications of Artificial Intelligence

The applications for artificial intelligence are endless. The technology can be applied to many different sectors and industries. AI is being tested and used in the healthcare industry for suggesting drug dosages, identifying treatments, and for aiding in surgical procedures in the operating room.

Other examples of machines with artificial intelligence include computers that play chess and self-driving cars. Each of these machines must weigh the consequences of any action they take, as each action will impact the end result. In chess, the end result is winning the game. For self-driving cars, the computer system must account for all external data and compute it to act in a way that prevents a collision.

Artificial intelligence also has applications in the financial industry, where it is used to detect and flag activity in banking and finance such as unusual debit card usage and large account deposits—all of which help a bank’s fraud department. Applications for AI are also being used to help streamline and make trading easier. This is done by making supply, demand, and pricing of securities easier to estimate.

Types of Artificial Intelligence

Artificial intelligence can be divided into two different categories: weak and strong. Weak artificial intelligence embodies a system designed to carry out one particular job. Weak AI systems include video games such as the chess example from above and personal assistants such as Amazon’s Alexa and Apple’s Siri. You ask the assistant a question, and it answers it for you.

Strong artificial intelligence systems are systems that carry on the tasks considered to be human-like. These tend to be more complex and complicated systems. They are programmed to handle situations in which they may be required to problem solve without having a person intervene. These kinds of systems can be found in applications like self-driving cars or in hospital operating rooms.

Special Considerations

Since its beginning, artificial intelligence has come under scrutiny from scientists and the public alike. One common theme is the idea that machines will become so highly developed that humans will not be able to keep up and they will take off on their own, redesigning themselves at an exponential rate.

Another is that machines can hack into people’s privacy and even be weaponized. Other arguments debate the ethics of artificial intelligence and whether intelligent systems such as robots should be treated with the same rights as humans.

Self-driving cars have been fairly controversial as their machines tend to be designed for the lowest possible risk and the least casualties. If presented with a scenario of colliding with one person or another at the same time, these cars would calculate the option that would cause the least amount of damage.

Another contentious issue many people have with artificial intelligence is how it may affect human employment. With many industries looking to automate certain jobs through the use of intelligent machinery, there is a concern that people would be pushed out of the workforce. Self-driving cars may remove the need for taxis and car-share programs, while manufacturers may easily replace human labor with machines, making people’s skills obsolete.

What Is an Application Programming Interface (API)?

An application programming interface (API) is a set of programming codes that queries data, parse responses, and sends instructions between one software platform and another. APIs are used extensively in providing data services across a range of fields and contexts.

APIs have become increasingly popular tools, with the likes of Meta (formerly Facebook), Amazon, SalesForce, and many more establishing their own APIs that allow companies to access some of their services without having to fully migrate into their ecosystem. This new paradigm has led to the rise of what some experts call the “API economy,” a model that enhances a company’s bottom line by improving interoperability and thus creating new systems from existing ones.

In the domain of financial markets and trading, one may use an API to establish a connection between a set of automated trading algorithms and the trader’s preferred trading broker platform for the purpose of obtaining real-time quotes and pricing data or to place electronic trades.

Understanding Application Programming Interfaces (APIs)

APIs have become increasingly popular with the rise of automated trading systems. In the past, retail traders were forced to screen for opportunities in one application and separately place trades with their broker. Many retail brokers now provide APIs that enable traders to directly connect their screening software with the brokerage account to share real-time prices and place orders. Traders can even develop their own applications using programming languages like Python and execute trades using a broker’s API.

Two types of traders use broker APIs:

• Third-Party Applications – Many traders use third-party applications that require access to broker APIs for pricing data and placing trades. For example, MetaTrader is one of the most popular foreign exchange (forex) trading applications and requires API access to secure real-time pricing and place trades.
• Developer Applications – A growing number of traders develop their own automated trading systems, using programming languages like Python, and require a way to access pricing data and place trades.

Despite the apparent benefits of APIs, there are many risks to consider. Most APIs are provided to a broker’s customers free of charge, but there are some cases where traders may incur an extra fee. It’s important to understand these fees before using the API.

Traders should also be aware of any API limitations, including the potential for downtime, which could significantly affect trading results.

Where to Find APIs for Traders

The most popular brokers supporting API access in the traditional stock and futures markets include TradeStation, TDAmeritrade, and InteractiveBrokers, but many smaller brokers have expanded access over time. APIs are more common among forex brokers where third-party applications and trading systems—such as MetaTrader—have been commonly used for many years.

Many brokers provide online documentation for their APIs. Developers can find out exactly how to authenticate with the API, what data is available for consumption, how to place orders through the API, and other technical details. It’s essential to be familiar with these details before choosing a broker when looking for specific functionality.

Some brokers also provide libraries in various languages to make interaction with their API easier. For example, a broker may offer a Python library that provides a set of functions, or methods, for placing a trade rather than having to write your own functions to do so. This can help accelerate the development of trading systems and make them less costly to develop.

What Is Actuarial Science?

Actuarial science is a discipline that assesses financial risks in the insurance and finance fields, using mathematical and statistical methods. Actuarial science applies the mathematics of probability and statistics to define, analyze, and solve the financial implications of uncertain future events. Traditional actuarial science largely revolves around the analysis of mortality and the production of life tables, and the application of compound interest.

Understanding Actuarial Science

Actuarial science attempts to quantify the risk of an event occurring using probability analysis so that its financial impact can be determined. Actuarial science is typically used in the insurance industry by actuaries. Actuaries analyze mathematical models to predict or forecast the reasonableness of an event occurring so that an insurance company can allocate funds to pay out any claims that might result from the event. For example, studying mortality rates of individuals of a certain age would help insurance companies understand the likelihood or timeframe of paying out a life insurance policy.

Actuarial science became a formal mathematical discipline in the late 17th century with the increased demand for long-term insurance coverage. Actuarial science spans several interrelated subjects, including mathematics, probability theory, statistics, finance, economics, and computer science. Historically, actuarial science used deterministic models in the construction of tables and premiums. In the last 30 years, science has undergone revolutionary changes due to the proliferation of high-speed computers and the union of stochastic actuarial models with modern financial theory.

Applications of Actuarial Science

Life insurance and pension plans are the two main applications of actuarial science. However, actuarial science is also applied in the study of financial organizations to analyze their liabilities and improve financial decision-making. Actuaries employ this specialty science to evaluate the financial, economic, and other business applications of future events.

Insurance

In traditional life insurance, actuarial science focuses on the analysis of mortality, the production of life tables, and the application of compound interest, which is the accumulated interest from previous periods plus the interest on the principal investment. As a result, actuarial science can help develop policies for financial products such as annuities, which are investments that pay a fixed income stream. Actuarial science is also used to determine the various financial outcomes for investable assets held by non-profit corporations as a result of endowments. 

In health insurance, including employer-provided plans and social insurance, actuarial science includes analyzing rates of

• Disability in the population or the risk of a certain group of people becoming disabled
• Morbidity or the frequency and the extent to which a disease occurs in a population
• Mortality or mortality rate, which measures the number of deaths in a population that result from a specific disease or event
• Fertility or fertility rate, which measures the number of children born

For example, disability rates are determined for veterans that may have been wounded in the line of duty. Certain percentages are assigned to the extent of the disability to determine the payout from disability insurance.

Actuarial science is also applied to property, casualty, liability, and general insurance–instances in which coverage is generally provided on a renewable period, (such as yearly). Coverage can be canceled at the end of the period by either party.

Pensions

In the pension industry, actuarial science compares the costs of alternative strategies with regard to the design, funding, accounting, administration, and maintenance or redesign of pension plans. A pension plan is a defined-benefit plan, which is a type of retirement plan involving contributions from the employer to be set aside and paid out to the employees upon retirement.

Short-term and long-term bond rates greatly influence pension plans and their investment strategies. Bonds are debt instruments issued by governments and corporations that typically pay a periodic interest rate. For example, in a low-interest-rate environment, a pension plan might have difficulty earning income from the bonds that it has invested in, which increases the probability that the pension plan could run out of money.

Other factors impacting a pension plan’s viability include benefit arrangements, collective bargaining, the employer’s competitors, and changing demographics of the workforce. Tax laws and the policies of the Internal Revenue Service (IRS) regarding the calculation of pension surpluses also impact the finances of a pension plan. Additionally, economic conditions and trends in the financial markets can impact the probability of a pension plan remaining funded.

Universities and Professional Certifications

According to the Bureau of Labor Statistics, the number of actuaries employed is expected to grow 21% from 2021 to 2031. For this reason, many universities offer educational degrees and courses on actuarial science. In addition, there are professional designations for those interested in pursuing the field.

Universities

The Society of Actuaries identifies and reports colleges that meet one of three levels of recognition:

• UCAP-Introduction Curriculum: Universities that maintain course requirements for two professional actuarial exams in addition to having met other approved course requirements.
• UCAP-Advanced Curriculum: Universities that maintain course requirements for four professional exams in addition to having met other approved course requirements.
• Center of Actuarial Excellence: Universities that maintain eight specific requirements in connection with a variety of matters. This is the highest tier of competency identified by the SOA for a university.

As of December 2022, there are roughly 25 Center of Actuarial Excellence schools across the United States, Canda, Australia, Singapore, the United Kingdom, and China. Within the U.S., these schools include but are not limited to Brigham Young, Georgia State, Purdue, Connecticut, and Michigan.

Professional Designations and Credentials

There are a number of different professional designations an actuary can pursue to further gain credibility and proficiency in their field. The Casualty Actuarial Society offers the Associate (ACAS) and Fellow (FCAS) membership levels, each of the two with escalating requirements. For example, the ACAS credential can be achieved after passing six exams, while the FCAS is earned after nine exams. Areas of focus for the FCAS exam include:

• Probability
• Financial Mathematics
• Financial Economics
• Modern Actuarial Statistics
• Basic Techniques for Ratemaking and Estimating Claim Liabilities
• Regulation & Financial Reporting
• Policy Liabilities, Insurance Company Valuation, and Enterprise Risk Management
• Advanced Ratemaking

The Society of Actuaries promotes several different actuarial exams to demonstrate competency in the field.

• An Associate of the Society of Actuaries (ASA) demonstrates knowledge of fundamental concepts of modeling and managing risk. The examination requirements are changing as of Spring 2023, and the list of required examinations includes topics on predictive analysis, economics, probabilities, and financial markets.
• A Chartered Enterprise Risk Analyst (CERA) specializes in having knowledge in identifying, measuring, and managing risk in risk-bearing enterprises. Similar to the ASA requirements, the CERA requirements include a professional course covering code of conduct.
• A Fellow of the Society of Actuaries (FSA) demonstrates knowledge of financial decisions involving pensions, life insurance, health insurance, and investments. FSAs also must demonstrate in-depth knowledge and the application of appropriate techniques to these various areas.

The Bottom Line

Actuarial science is the study of mathematically predicting the probability of something happening in the future and assigning that outcome a financial value. Companies, pension funds, and insurance agencies rely on actuaries to develop models to assess areas of risk and devise policies to mitigate potential future challenges.