Category: Artificial Intelligence

Artificial intelligence has profoundly affected almost every business, including banking and finance. The integration of AI in banking apps and services has made the sector more customer-focused and technologically relevant.

AI-based solutions can help banks save money by enhancing efficiency and making judgements based on data that a human agent would find incomprehensible. Furthermore, intelligent algorithms may readily detect false data.

What are the use cases of AI in banking?

Artificial intelligence is already being used by banks and fintech companies in their services since it is becoming an essential part of the world we live in. You may benefit from the various advantages of the technology with the aid of these well-known banking-related AI apps. Let’s look at a few crucial applications of AI in banking.

Cybersecurity and fraud detection

Every day, a sizable number of digital transactions take place as customers use applications or online accounts to pay bills, withdraw cash, deposit checks, and do a variety of other tasks. Therefore, the financial sector needs to put more effort into cybersecurity and fraud detection.

This is where AI in banking becomes extremely practical and efficient. Artificial intelligence (AI) can help banks lower risks, track system problems, and improve the security of online financial transactions. AI and machine learning are able to detect fraudulent behaviour quickly and alert banks as well as customers.

Chatbots

Chatbots are one of the best instances of artificial intelligence applications in banking. Once deployed, they may work whenever they choose, unlike others who have regular office hours.

Additionally, they maintain a record of the usage patterns of certain clients. It facilitates their efficient comprehension of consumer requirements.

By integrating chatbots into their banking apps, banks can ensure that they are reachable to their customers around the clock. Additionally, by understanding consumer behaviour, chatbots may offer customised customer care and propose suitable financial services and products.

Loan and Credit decisions

Banks have started integrating AI-based technologies to help them make better, safer, and more profitable lending and credit decisions. A person’s or company’s creditworthiness is now solely taken into account by many banks based on their credit history, credit ratings, and customer references.

It is impossible to overlook the reality that these credit reporting systems routinely contain errors, exclude real-world transaction histories, and identify creditors inaccurately.

An AI-based loan and credit system can study the patterns of conduct of customers with limited credit history to determine their creditworthiness. Additionally, the system alerts banks to certain acts that can increase the danger of default. In conclusion, these technologies are fundamentally changing how consumer finance will be carried out in the future.

Tracking market trends

Thanks to artificial intelligence in financial services, banks can analyse massive volumes of data and anticipate the forthcoming changes in markets, currencies, and stocks. Modern machine learning techniques can assess market mood and provide investment recommendations.

AI in banking may also alert users to potential risks and suggest when to buy stocks. Due to its high data processing capacity, its cutting-edge technology also aids in accelerating decision-making and facilitates trading for both banks and their clients.

Data collection and analysis

Millions of transactions are recorded daily by financial and banking companies. The volume of information generated makes it challenging for staff to gather and record it. It becomes challenging to organise and accurately capture such a big volume of data.

In such cases, effective data collection and analysis can be facilitated by creative AI-based solutions. As a result, the entire user experience is improved. Additionally, the information may be used to spot fraud or render credit decisions.

Customer experience 

Consumers are constantly looking for more practical experiences. For instance, the reason ATMs are so popular is that they give customers access to essential services like cash withdrawals and deposits even when banks are closed.

This simplicity of use has only encouraged further innovation. Customers may now open bank accounts on their smartphones from the convenience of their homes.

Integration of artificial intelligence will enhance customer satisfaction and user comfort in banking and financial services. Know Your Customer (KYC) data capture is accelerated by AI technology, which also removes errors. Additionally, timely product and financial offers can be made.

By using AI to automate qualifying for situations like applying for a personal loan or credit, clients may skip the bother of going through the entire procedure manually. Additionally, technologies powered by AI can expedite approval procedures for services like loan disbursement.

Additionally, AI banking supports accurate client data collecting for error-free account creation, providing a great customer experience.

Risk management

Exchange rate fluctuations, natural disasters, and political unrest are just a few examples of external global factors that have a substantial influence on the banking and financial sectors. In these unsettling times, it is crucial to proceed with extra caution while making business decisions. AI-driven analytics may offer a somewhat accurate prediction of upcoming events, helping you to stay organised and make choices on time.

By estimating the possibility that a consumer will fail on a loan, AI helps in spotting risky applications. It predicts this future conduct by looking at previous behaviour patterns and smartphone data.

Regulatory compliance

The banking sector is among those with the strictest regulations in the world. It is against the law for banking clients to use banks to conduct financial crimes, and governments utilise their regulatory powers to ensure that banks have proper risk profiles and don’t have widespread defaults.

Banks frequently hire internal compliance personnel to deal with these problems, but manual fixes are much more time- and money-consuming. To comply with the compliance standards, which are often modified, banks must continuously upgrade their processes and workflows.

AI is utilised to interpret new compliance criteria for financial institutions and improve their decision-making using deep learning and Natural Language Processing (NLP). Even though it cannot replace a compliance analyst, AI banking can improve its processes.

Predictive analytics

Two of the most common applications for AI are predictive analytics and general-purpose semantic and natural language applications. Data may contain unique connections and patterns that AI may spot that were previously undetectable by conventional technology.

These patterns could indicate underutilised cross-sell or sales opportunities, operational data measurements, or even factors that affect revenue.

Process automation

Robotic process automation (RPA) algorithms increase operational efficiency and accuracy while reducing costs by automating time-consuming repetitive procedures. Users can now focus on more difficult activities requiring human involvement.

Banking businesses are now successfully using RPA to speed up transactions and increase efficiency. OCR technology, for instance, may be used to inspect documents and extract data from them far faster than people can.

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Artificial intelligence (AI) and machine learning are used in the financial industry for a range of applications, including task automation, fraud detection, and chatbot assistants. According to a recent AI in Banking survey, the vast majority of banks (80%) are well aware of the potential benefits that AI may provide.

The usage of artificial intelligence (AI) by financial institutions (FIs) will accelerate as technology progresses, consumer acceptance grows, and regulatory environments shift. By giving their customers access to their accounts and financial advising services around the clock, banks may greatly enhance the customer experience and minimise time-consuming operations.

What is Artificial Intelligence (AI)?

Artificial intelligence refers to systems or technologies that execute tasks by mimicking human intellect. AI is a very important economic asset since it is intended to significantly enhance human abilities and contributions.

What is Artificial Intelligence in Finance?

Artificial intelligence (AI) in finance is the application of technology such as machine learning (ML) to improve how financial organisations evaluate, manage, invest, and safeguard money.

How is AI driving innovation in Finance?

Financial operations have historically relied significantly on human labour, including data input, data collecting, data verification, consolidation, and reporting. The finance function tends to be expensive, time-consuming, and sluggish to change due to all of these manual tasks. At the same time, a lot of financial procedures are predictable and well-defined, which makes them excellent candidates for AI automation.

Companies were able to concentrate and standardise their financial operations thanks to the development of ERP systems. Early automation using AI was rule-based, which meant that when a transaction or input was completed, it would be processed according to a set of preset rules. These systems automate financial activities, but they lack the agility of current AI-based automation, need a lot of human maintenance, and update slowly. In contrast to rule-based automation, AI can handle more complicated circumstances, such as the total automation of dull, manual tasks.

Increasing automation equals greater accuracy in your financial procedures. With people, high-volume, boring operations like invoice input can cause weariness, burnout, and mistakes. Computers, on the other hand, are not bound by these limitations. They may also handle far more transactions in a particular time span. As a consequence, the finance team has better data to work with and more time to focus on putting that data to use.

What are the uses of artificial intelligence in financial services?

Examples of AI in finance

Companies now use AI-driven technologies to help them stay up with the rapid pace of development. 85% of company leaders desire assistance from artificial intelligence, according to a 2021 survey.

These are three typical ways businesses are utilising artificial intelligence.

First, businesses are embracing artificial intelligence to offer smart categorization and smart recognition, automating manual procedures like accounts payable processes.

Second, automated financial closure procedures allow businesses to refocus staff efforts from manual data gathering, reporting, and consolidation to analysis, strategy, and action. Scenario modelling and unbiased forecasting are key components of smart prediction.

Lastly, businesses are introducing AI-guided digital assistants that facilitate content discovery and task completion wherever you are. Finance departments, for example, may use digital assistants to alert teams when spending is out of compliance or to automatically submit expense reports for speedier payment.

With major economic benefits and demand from tech-savvy consumers in mind, let’s explore how FIs are using AI algorithms across all financial services:

AI in Personal Finance

Because customers are ravenous for financial independence, the ability to regulate one’s financial health is pushing the adoption of AI in personal finance. AI is a must-have for every financial institution that wants to be a market leader, whether it’s delivering natural language processing-powered chatbots with 24/7 financial advice or personalising insights for wealth management solutions.

AI in Consumer Finance

The ability of artificial intelligence to detect and prevent fraud and cyberattacks is one of the most critical business cases for artificial intelligence in banking. Customers need safe accounts from banks and other financial institutions, especially with online payment fraud losses anticipated to reach $48 billion per year by 2023, according to Insider Intelligence. AI has the capacity to examine and identify abnormalities in patterns that humans might otherwise miss.

AI in Corporate Finance

AI has a significant impact on corporate finance because it can more precisely detect and analyse credit risks. Machine learning and other artificial intelligence (AI) technology may improve loan underwriting and minimise financial risk for firms looking to increase their value. Although corporate accountants, analysts, treasurers, and investors aim for long-term growth, artificial intelligence (AI) may help minimise financial crime by improving fraud detection and detecting anomalous behaviour.

What are the benefits of AI in Finance?

Using AI in banking has enormous advantages for work automation, fraud detection, and providing individualised suggestions. The following are some of the ways AI use cases in the front and middle office can revolutionise the financial sector:

Enabling seamless & round-the-clock Customer Engagements

AI enables financial companies to speed up and automate formerly manual, time-consuming operations like market research.

In order to estimate future performance and discover patterns, AI can swiftly analyse enormous amounts of data. This enables investors to track investment growth and assess possible risks.

Minimising the need for tedious effort

AI and ML may enhance the whole customer experience for banking consumers. The advent of online banking (contactless banking) reduces the need for face-to-face interactions, yet the move to the virtual world may increase endpoint vulnerabilities (e.g., on cell phones, computers, and mobile devices).

Several fundamental banking transactions, such as payments, deposits, transfers, and customer support inquiries, may be automated using AI. AI can also handle credit card and loan application processes, including approval and denial, with near-instant answers.

Reducing false positives and human error

Personal data can be mined and utilised to decide coverage and premiums in the insurance industry.

AI may also be utilised in cybersecurity, especially to detect fraudulent transactions. AI may highlight aberrant behaviour, automatically inform both the institution and the consumer to verify the purchase or transfer in real-time, and take action to fix it by continuously monitoring purchase behaviour and comparing it to previous data.

Saving Revenue

By 2025, North American banks may save $70 billion by automating middle-office functions using AI. In the coming years, the implementation of AI technology in banks is anticipated to yield significant cost savings. According to recent projections, the potential savings from AI applications in banks could reach an impressive $447 billion by 2023. Interestingly, the bulk of these savings, which is estimated to be around $416 billion, is expected to come from the front and middle offices of banks. These numbers reflect the tremendous potential for AI to revolutionize the way banks operate and serve their customers, leading to increased efficiency and profitability.

What are the risks of not adopting artificial intelligence in finance?

According to the previously cited “Money and Machines” survey, 87% of business leaders feel that firms that do not rethink finance procedures would face threats such as:

• Losing ground to competitors by 44%
• 36% of workers who are more stressed
• 36% of reports are inaccurate.
• 35% decrease in staff productivity

Businesses that take their time using AI risk becoming less appealing to the next generation of financial experts. 83% of millennials and 79% of Generation Z respondents stated they would prefer a robot over their company’s financial personnel. Millennials are nearly four times as likely as Baby Boomers to want to work for a firm that uses artificial intelligence to handle money.

What does the future of AI look like for the Finance and FinTech sectors?

Due to increased client demand for digital goods and the threat of tech-savvy startups, financial institutions (FIs) are swiftly embracing digital services; by 2021, worldwide banks’ IT investment will climb to $297 billion.

FIs are under pressure to increase their IT and AI expenses in order to meet increased digital needs as millennials and Gen Zers overtake baby boomers as the largest target customer group for banks in the United States. Because 78% of millennials avoid visiting a branch if possible, these younger clients favour internet banking alternatives.

While the shift from traditional banking channels to online and mobile banking was already underway prior to the pandemic due to increased opportunity among digitally native consumers, the coronavirus dramatically accelerated the shift as stay-at-home orders were implemented across the country and consumers sought more self-service options. According to Insider Intelligence, internet and mobile banking penetration among US customers will climb to 72.8% and 58.1%, respectively, by 2024, making AI deployment important for FIs aiming to be successful and competitive in the developing sector.

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In previous part, we understood what is and adversarial attack and how it can be classified based on various attributes. (In case you missed it, you can read it here). In this part, we will study some of the most common types of attacks in detail.

Outline

In this part we will overview some of the most common attacks on image classifiers and implement a very popular method called FGSM (Fast Gradient Sign Method) attack proposed in Goodfellow et al. and understand how it works as well as how it challenges the notion of non-linearity of neural networks being the reason behind success of adversarial attacks.

L-BFGS Attack

This was one of the earliest attacks where Szegedy et al. first discovered the vulnerability of deep visual models to adversarial perturbations by solving for the following optimization problem:

where we are trying to minimize ρ (which is the adversary signal) with second-norm. If we look closely, this equation is similar to equation described in part — 1, with norm-value p=2.

For this problem, approximate solution was computed by Szegedy et al. with the Limited Memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithm, upon which this method is named. However, solving this equation for large number of examples, is computationally prohibitive, which is addressed in next method. That is when the next method comes into picture.

FGSM Attack

The FGSM is among the most influential attacks in the existing literature, especially in the white-box setup. Its core concept of performing gradient ascend over the model’s loss surface to fool it, is the basis for a plethora of adversarial attacks. Many follow-up attacks can be strongly related to the original idea of FGSM. The most common image showing adversarial attacks has been of FGSM attack:

The FGSM is a one-step gradient-based method that computes norm-bounded perturbations, focusing on the ‘efficiency’ of perturbation computation rather than achieving high fooling rates. Goodfellow et al. also used this attack to corroborate their linearity hypothesis, which considers the linear behavior of the modern neural networks in high dimension spaces (induced by ReLUs) as a sufficient reason for their vulnerability to adversarial perturbations. At the time, the linearity hypothesis was in sharp contrast to the developing idea that adversarial vulnerability was a result of high ‘non-linearity’ of the complex modern networks.

Goodfellow et al. claimed that adversarial examples expose fundamental blind spots in our training algorithms. They also claimed that linear behavior in high-dimensional spaces is sufficient to cause adversarial examples. And using this, they designed a fast method of generating adversarial examples that makes adversarial training practical.

Linear explanation of adversarial examples

We start with explaining the existence of adversarial examples for linear models. Since the precision of the features is limited, we will see how the classifier can be forced to respond differently to an input x than to an adversarial input x̃ = x + η if every element of the perturbation η is smaller than the precision of the features.

Adversarial attacks for linear model

Thus, for high dimensional problems, we can make many infinitesimal changes to the input that will add up to one large change to the output.

Linear Perturbation of Non-Linear Models

The linear view of adversarial examples suggests a fast way of generating them. We hypothesize that neural networks are too linear to resist linear adversarial perturbation. LSTMs, ReLUs, and maxout networks are all intentionally designed to behave in very linear ways, so that they are easier to optimize. More nonlinear models such as sigmoid networks are carefully tuned to spend most of their time in the non-saturating, more linear regime for the same reason. This linear behavior suggests that cheap, analytical perturbations of a linear model should also damage neural networks. Let us see how adversarial examples can be generated for the neural networks.

We refer to this as the “fast gradient sign method” of generating adversarial examples. Note that the required gradient can be computed efficiently using backpropagation.

Code Implementation for FGSM

Let us see how we can implement this in code. We will be using code from this link, which is part of tensorflow official documentation. We will analyze this function ‘create_adversarial_pattern’, as it implements the crux of the paper, i.e. calculates the gradient sign.

def create_adversarial_pattern(input_image, input_label):
with tf.GradientTape() as tape:
tape.watch(input_image)
prediction = pretrained_model(input_image)
loss = loss_object(input_label, prediction)

# Get the gradients of the loss w.r.t to the input image.
gradient = tape.gradient(loss, input_image)
# Get the sign of the gradients to create the perturbation
signed_grad = tf.sign(gradient)
return signed_grad

# codel_url: https://www.tensorflow.org/tutorials/generative/adversarial_fgsm

Using below lines, we are basically asking tensorflow to keep track of computations related to ‘input_image’

with tf.GradientTape() as tape:
tape.watch(input_image)

Using below two lines, we are making predictions for ‘input_image’, and calculating the loss related to this prediction.

prediction = pretrained_model(input_image)
loss = loss_object(input_label, prediction)

Using below lines, we are calculating the gradient of loss wrt input image, this part contributes to the ‘gradient’ term in the FGSM (Fast Gradient Sign Method)

gradient = tape.gradient(loss, input_image)

Once we have gradients, we need to calculate use ‘sign’ function of gradient, i.e, sign(gradient), and we have ‘gradient sign’ term in FGSM (Fast Gradient Sign Method). Below is the input image, for which we are calculation adversarial perturbation (η).

Calculated adversarial perturbation (η) using FGSM method comes out as:

Adversarial image to fool the model is calculated using the below code:

adv_x = image + eps*perturbations

Here is what the generated result looks like for different values of ϵ

Goodfellow et al. concluded the following things as a result of this experiment:

• Adversarial examples can be explained as a property of high-dimensional dot products. They are a result of models being too linear, rather than too nonlinear.
• The generalization of adversarial examples across different models can be explained as a result of adversarial perturbations being highly aligned with the weight vectors of a model, and different models learning similar functions when trained to perform the same task.
• The direction of perturbation, rather than the specific point in space, matters most. Space is not full of pockets of adversarial examples that finely tile the reals like the rational numbers.
• Because it is the direction that matters most, adversarial perturbations generalize across different clean examples.

Other attacks

• Some other attacks that built on FGSM attack or had similar methodology are: Basic Iterative Method (BIM), The Projected Gradient Descent (PGD) attack,The DeepFool Attack, The C&W Attack.
• JSMA & One-pixel Attack: Whereas most of the early attacks focused on perturbing a clean image holistically by limiting perturbation by minimizing second-normor infinity-norm of the perturbations, the Jacobian-based Saliency Map Attack (JSMA) and One-pixel attack deviate from this practice by restricting the perturbations to smaller regions of the image.
• Universal Adversarial Perturbations:The above-mentioned methods compute adversarial perturbations that fool a target model on a specific image. Moosavi-Dezfooli et al. focused on computing image-agnostic perturbations that could fool the model on any image with a high probability as shown in below image:

Thus, we studied FGSM attack in details and understood its implementation in tensorflow. We also overviewed some other attacks on image classification. In further parts, we will be moving beyond classification and seeing how adversarial attacks can be performed on other tasks such as Face Recognition, Object detection, Object Tracking and how they can affect the real world in several ways.

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Deep Learning has proven to be a very efficient tool in recent times when it comes to solving challenging problems across various fields such as healthcare (computer-aided assessment, drug discovery), financial services (fraud detection), automobiles (self-driving automobiles, robotics), communications (news aggregation and misinformation detection), as well as other day-to-day utility services (such as virtual assistants, language translation, information extraction)

Deep Learning, on the other hand, is now proven to be sensitive to adversarial attacks that can alter its predictions by introducing practically imperceptible disturbances in sounds, pictures, or videos. In this series of blogs, we will learn about adversarial attacks and how they attempt to influence deep learning models in order to obtain the desired result.

Picture 1: A few illustrations of hostile actions

• By adding a little noise, a panda was mistaken for a gibbon.
• A stop sign was mistaken for a speed restriction sign.
• A person wearing a certain pattern was not noticed.

Outline

Prior to understanding    and the formal specification of the issue statement, we will first define several technical terms that frequently appear in publications. We shall divide the attacks into numerous groups based on a variety of characteristics in the next section.

Common terminologies and definitions:

Here, we’ll attempt to clarify some of the phrases that are used often in publications about this attack and throughout this essay.

• An adversarial example or picture is one that has been purposefully altered to lead to erroneous model predictions. The model receives this example (or a number of similar instances) as input.
• The element of an adversarial example or picture that results in an inaccurate prediction is called an adversarial perturbation. It frequently looks like low-magnitude additive noise.
• An adversarial example is being made by the agent or the attacker. Alternately, albeit much less frequently, the adversary is sometimes referred to as the hostile signal/perturbation.
• Defence/hostile defence is a general word that refers to any method that increases a model’s resilience, as well as external or internal systems to identify adversarial signals and image processing to counteract the effects of input modifications that might be considered adversarial.
• Target image: This is a blatant instance of an opponent manipulating a picture.
• Target label: This is the antagonistic example’s (desired) inaccurate label. The phrase applies more to categorization issues.

What is an Adversarial attack?

An adversarial attack uses several strategies to harvest important data from the deep learning model or manipulate the input picture as little as possible in an effort to fool the network into producing the desired output. The target deep learning models, their weights, and the training dataset may be accessible to the attacker in a variety of ways. Attacks can be divided into many types, some of which have been mentioned in the article later, depending on the level of access the attacker has.

The following equation can be used to formalise the issue:

The pre-defined scalar threshold () is frequently kept at a low value, so the difference seems to a human subject to be extremely little. Similar to p, the most frequent values of p are often one or two, however, this is not a restriction.

Classification of adversarial attacks

A generic data processing pipeline may be used to visualise a machine learning system (see Figure below). At inference, (a) input features are gathered from sensors or data sources, (b) processed digitally, (c) utilised by the model to generate an output, and (d) the outcome is transmitted to an external system or user and used to take action. Take a look at Figure 1 for an example of a general pipeline, an autonomous car, and network intrusion detection systems (middle and bottom). We will attempt to comprehend alternative attacks based on their impact on this general pipeline given that the attack might be of variable range depending on the objectives of an opponent and his capabilities in accessing the model and data.

Picture 2: ML system pipeline in general (with examples)

Attack surface, adversarial capabilities, and adversarial aims are the three characteristics used to categorise attacks.

Attack surface

An attacker can decide which step (or surface) of a pipeline to target in order to accomplish his or her objective given a pipeline of phases. The following is a sketch of the primary attack scenarios detected by the attack surface:

1. Evasion attack: The most frequent attack in an adversarial context is this one. During the testing phase, the adversary modifies harmful samples in an effort to go around the system. This option makes no assumptions about how the training data will be affected.
2. Poisoning attack: In order to jeopardise the entire learning process, this kind of attack, also known as contamination of the training data, is carried out during the training phase by putting carefully created samples into the system to poison it.
3. Exploratory attack: The training dataset is unaffected by these attacks. When given black-box access to the model, they attempt to learn as much as they can about the underlying system’s learning mechanism and the patterns in the training data.

Adversarial capabilities:

It speaks to the volume of knowledge an opponent has about the system. By further separating them into inference and training phases, we may better understand the breadth of attacker capabilities.

Training phase capabilities:

The majority of attacks are carried out during the training phase by directly changing the dataset in order to learn, influence, or corrupt the model. Based on the adversarial capabilities, the attack tactics are roughly divided into the following three categories:

▹ Data injection: when the adversary is unable to access the learning algorithm or training data but is still able to add fresh data to the training set. By including hostile samples in the training, he can taint the target model.

▹ Data modification: The training data is completely accessible to the adversary but not the learning algorithm. By altering the data before it is used to train the algorithm, he directly poisons the training data.

▹ Logic corruption: The learning algorithm is susceptible to interference from the opponent. Creating a counterplan against them becomes exceedingly challenging.

2. Testing phase capabilities:

Instead of interfering with the targeted model during testing, adversarial attacks cause it to provide the wrong results. These can either be considered white-box or black-box.

▹White-box attack: An adversary using a white-box attack on a machine learning model has complete knowledge about the model being used (for example, the kind of neural network and the number of layers, details on the training procedure, and parameters () of the fully trained model architecture). This data is used by the adversary to examine the feature space where the model may be weak, i.e., where the model has a high mistake rate. For a white-box assault, access to internal model weights equates to an extremely potent adversarial attack.

▹Black-box attack: Black-box attacks leverage information about the settings and previous inputs to take advantage of the model without assuming any prior knowledge of the model. The three kinds of black-box attacks include strict black-box attacks, adaptive black-box attacks, and non-adaptive black-box attacks.

Adversarial goals

Attacks may be categorised into the following four groups according to the adversary’s goal:

1. Confidence reduction: The adversary seeks to lower the target model’s forecast confidence. For instance, a legal image of a “stop” sign can be predicted with less certainty and with a lower likelihood of class membership.
2. Mis‐classification: The adversary tries to change an input example’s output categorization to belong to a different class. For instance, any other class other than the class of a stop sign will be predicted for a real image of a “stop” sign.
3. Targeted misclassification: The adversary attempts to manipulate the inputs so that the model generates the output of a specific target class.
4. Source/target misclassification: The adversary attempts to assign a certain input source to a predetermined target class. For instance, the classification model will forecast that the input picture of the “stop” sign represents the “go” sign.

Using the flowchart below, all kinds and subcategories of adversarial assaults may be summarised:

Picture 3: Flowchart for several sorts of adversary attacks

As a result, we now know what an adversarial assault is and how many distinct ways it may be categorised based on various attributes. In the following sections, we’ll look at some of the most typical attack types and how adversarial attacks may be used for tasks like object identification, object tracking, NLP, and audio in addition to image classification.

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In the 1960s, Xerox renewed its entire business model based on a single premise: Customers don’t want products. They want services. Having reached its tether’s end, the brand pivoted and is now one of the most successful providers of digital printing solutions. As Renee Montagne, the CEO of Xerox, in 2012 aptly stated, “If you don’t transform, you’re stuck.”

Our touched on stories of AI and digital transformation. While traditionally slower to innovate than others, the telecom industry is waking up to the benefits of rapid digitalization, aggressive automation, and AI-led ecosystems.

So, what’s next for telcos, and how do they leapfrog their AI programs? By transforming the entire operational process pipeline with AI at the core. Here are 5 ways to do this:

1. Make AI Pervasive in Networks

Software-defined networking (SDN) and network functions virtualization (NFV) help telcos rewire their networks dynamically for unprecedented gains. The applications rise beyond capacity planning and network management. For context, think of how replacing an appliance, like a ceiling fan, from a mechanical one to a smart fan alters user behavior as well as fan performance. Customers can download an app to start and stop the fan remotely, change its speed, monitor energy efficiency, and get recommendations for optimal settings. All of this is done remotely, delivering extreme user convenience. The seller, on the other hand, gets insights into performance to predict failures, diagnose issues, and preemptively schedule maintenance, saving on ad-hoc costs. Similarly, SDN facilitates a revolution in network provisioning and customer experience.

2. Create AI-centric Business Models.

More than providing infrastructure to run various innovative services, telcos must join the flurry of disruption by doing more with AI. One fundamental change is the ecosystem mindset that spawns new partnership models whereby telcos also grab a slice of the market thanks to their vast customer reach. As the saying goes, “Alone, I can run fast. Together, we can run far.” Telecom is ripe with examples of this. Consider how mobile wallets have bled into the FinTech space, nudging telcos into evaluating how they can offer solutions for fraud, encryption, and Anti-Money Laundering (AML).

3. Stay Agile with Open Architecture.

An example in the previous blog illustrated how small shops can offer seamless customer onboarding. One of the levers is back-end data encryption for the digital verification of a customer’s credentials, which is impossible without open architecture. To simplify, one could compare open architecture to the standard-issue fuel inlet in all motor vehicles. No matter the automotive brand, all vehicles are outfitted with a single type of inlet valve, allowing the motorist to refill fuel at any fuel station. Similarly, the open architecture enables telcos to move away from proprietary software to those that grant fast, secure, and seamless interconnections to a larger ecosystem.

Telcos can monetize data in resourceful ways, as in the case of alternate credit scoring using telecom data to support microfinance loans and creditworthiness to numerous non-banked populations where there is no conventional credit bureau. Open infrastructure and architecture equips telcos to wield innovations such as the movement towards Open RAN or the development of Open APIs by TMForum. It also streamlines collaborations among vendors so telcos can onboard partners and bundle services and packages with agility. Modern mobile apps of traditional telcos is a classic case, replete with non-telco services such as utility payments, mobile wallets, media and content, eCommerce, OTT subscriptions, and more.

4. Strategize for AI-driven Sales, Channel, and Supply Chain Management.

Indian insurance behemoth Life Insurance Corporation (LIC) set a precedent in how efficacious indirect channel marketing is when it empowered nearly 1.3 billion agents across India to sell its policies raking in nearly 96% of the titan’s revenues. Traditionally, telcos have not fully monetized indirect channels. With AI, this will change. Through cost-effective and seamless onboarding via digital apps and robust security protocols, AI can channelize visibility to new subscribers. For instance, when a customer books a flight ticket, telcos can promptly offer roaming plans customized to the subscriber based on their usage patterns. Similarly, AI can also revamp supply chain operations by infusing intelligent sourcing practices that respond intuitively to unpredictable market forces. The widespread impact on food supplies and other essential manufacturing raw materials due to unrest in Ukraine is a prime example of why diversified and intelligent supply chains are essential.

5. Curate Frictionless Customer Experiences.

Finally, all of this will bring to bear delightful customer experiences. As telcos use AI to reimagine their operations and processes, models and infrastructure, services, and products, it will have a game-changing impact on customers. Customers will not only experience first-hand the frictionless, delightful interactions crafted via AI but also cement their loyalty to a telecom provider that helps them live better lives and that prioritizes their conveniences and preferences – all in one single window.

Imagine the opportunities. And now, reimagine them with AI.

Subex is at the forefront of driving AI-led transformation. To watch a demo or learn how we help you revolutionize your business with AI, reach out to us at

hypersense@subex.com

A few years ago, if you were in India and visited any of the mom-and-pop mobile shops to buy a new SIM card, you would be presented with forms, asked to submit photocopies and a passport-size photograph, and to physically sign a document. Paperwork was then dispatched to another data entry center, an appointment date was set to verify your address, and after a few days of processing, your SIM was activated.

Today, the entire workflow takes a mere few minutes. First, you choose your number and your package. Then, present your Aadhaar, which is scanned using its QR code, snap a picture on-the-spot to verify it is you, validate your fingerprint with a nifty little biometric machine, and receive your new SIM, which is activated and ready to go.

At first, this scenario may appear like digitalization on steroids. But in fact, it is the organic shift of digitalization towards AI that enables intricate and differentiated experiences.

We are all in the business of technology.

Nearly every industry is brimming with examples of disruptive market trends driven by agile players. Think about the spate of acquisitions in the US where forward-thinking Japanese players bought out their lagging competitors who couldn’t respond to change fast enough.

If we look at Tesla, a classic disruptor in the technology space, we can see how different their approach is. Their problem statement was not to build a car; it was to offer mobility, convenience, and safety. And they are eagerly curious to leverage the most cutting-edge technologies to achieve all of this. With the power of AI, they are pioneers in their own right in the autonomous driving and electric vehicle market and are leading the market although there were so many other companies prior to them who launched electric cars. They have also created a channel to resell their cars, unlocking a new revenue stream for the brand. Tesla is unafraid of change and is constantly reinventing itself, its products, and its models through the latest tech.

In 2015, Anand Mahindra, Chairperson of Mahindra Group, displayed sharp foresight when he tweeted, “The age of access being offered by taxi-hailing apps like Uber and Ola is the biggest potential threat to the auto industry.”

And he was right.

Platforms like Uber, Lyft, Rideshare, Zoomcar, etc., have transformed the global automotive industry from being an ownership-driven one to on-demand mobility. It gave users budget-friendly travel options rather than simply buying a vehicle, thereby reaping multi-fold benefits: riders can save on down payments, EMI, parking fees, maintenance, depreciation, and more, while remaining mobile in the most convenient way. Similarly, the next generation of competition for telcos is not going to be from other telcos but from an army of digital enterprises offering a wide bouquet of services and experiences that customers are eager to lap up.

Everybody benefits.

It is crucial to remember that the power of AI lies not in simply digitizing a few workflows and automating processes for marginal efficiency gains. Instead, organizations realize the actual value of AI when they pan their sights outwards to visualize the entire operations landscape and reshape these, putting AI at the core.

With AI, we are seeing a mindset of openness and sharing, which is creating profound shifts within industries and needs to be highlighted because, more than competitiveness, companies know that collaboration is what steers success today. The market share for disruptive services is too large to be monopolized by a single entity. Instead, early AI adopters are nurturing holistic digital ecosystems where AI-led innovation facilitates interoperable infrastructure, effective billing mechanisms, transparent revenue sharing agreements, strong governance frameworks, and robust security protocols.

So, widen your AI lens.

Some forward-thinking telecom operators have jumped on the AI bandwagon to reach more subscribers through untapped channels and accelerate onboarding through frictionless, instant, and secure workflows.

Consider how T-Mobile is on a mission to build networks for the future. AT&T uses AI/ML to understand how climate change impacts service continuity. Telefonica leverages AI to craft immersive and intelligent living room experiences for movie watchers. Verizon 5G is grabbing the reins of Industry 4.0 by enabling smart factories through automated industrial machinery.

The use cases keep growing: AI technologies like natural language processing, face trace, liveness detection, and face match can greatly streamline governance by instantly validating ID proof against applicants and cross-checking authenticity. ML algorithms can mine data to understand customer preferences and personalize offers within seconds.

Here’s an excellent place to start.

Go back to the beginning. Relook at your business problem statements as a whole, rather than its components, and ask yourself:

• Where do redundancies lie, and how much can we eliminate?
• Where are inefficiencies costing us, and how can we optimize productivity?
• What are the highest cost drivers, and where can we use AI to slash this?
• Is there an entirely new way of performing this process that leverages everything AI stands for?

The answers to these questions give organizations the key elements to probe AI’s value beyond incremental gains. With so much innovation happening in the telco domain – think 5G, IoT, the metaverse – telcos must push the boundaries of their imagination. Indeed, AI helps telcos do one of two things:

1) Remain a telco that does better – They can use AI to improve the service stack, like faster broadband connectivity through 5G, and achieve incremental benefits from offerings like IoT packages to enterprises. Such point solutions will certainly deliver value like revenue and efficiency gains, albeit in a marginal manner.

2) Transform into a tech-co that disrupts the ecosystem – They can unlock boundless opportunities to do much more than previously imagined by crafting new journeys, curating new revenue streams, and taking pole position as an enabler driver than a follower of the AI revolution.

Which would you choose?

Note: This is a two-part blog series. Stay tuned for the second blog that dives into how telcos can reimagine AI.

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