Machine learning and AI have numerous potential benefits for risk management and security-related use cases. Many AI risk management offerings rely on the cloud's mass computing scale, where large amounts of unstructured data can be analyzed and processed quickly.
Financial crime becomes more sophisticated each year, new malware emerges, and fraud losses increase. Add to that a constantly changing regulatory environment and hefty non-compliance penalties, and financial institutions face an increasingly complex risk landscape.
(Also Read: Applications of AI in Finance)
Banks, insurance companies, asset managers, and other industry players must reconsider their approach to financial risk management in order to compete in the new environment.
This is where artificial intelligence can come in handy. AI can augment human-led risk management activities with advanced analytical capabilities to achieve better results much faster.
The ability of machine learning models to analyze large amounts of data - both structured or unstructured - can improve analytical capabilities in risk management and compliance, allowing risk managers in financial institutions to identify risks more effectively and timely, make more informed decisions, and reduce the risk of banking.
Numerous AI applications in the financial services ecosystem can identify patterns and connections that humans cannot, allowing for the improvement and augmentation of financial business processes.
AI-powered risk management analytics can assist organizations in evaluating the following:
1. Perplexing circumstances or situations
2. The likelihood of occurrence of a condition or situation based on context; and
3. The consequences of the occurrence, i.e., the possible outcomes
AI-powered risk management tools are frequently integrated into security automation workflows. They can also assist security leaders in making decisions during incidents, business continuity planning, fraud investigations, and other situations.
(Also Read: Types of Financial Risks)
Watch this video from SAS on “Is AI really a gamechanger?”
AI can help risk management and mitigation processes and practices in a variety of ways. The five most common use cases today are as follows:
Threat intelligence data sheds light on things like attacker sources, indicators of compromise, behavioral trends associated with cloud account use, and attacks against various types of cloud services.
Threat intelligence feeds can be aggregated, analyzed at scale with cloud-based machine learning engines, and processed for likelihood and predictability models.
With the rise of account hijacking and ransomware infections, security teams may find that more rapid data analysis and predictive intelligence prove invaluable.
Massive amounts of log data and other events are generated. Security teams must be able to recognize specific indicators quickly, see patterns of events as they occur, and detect events occurring in cloud environments.
Machine learning and artificial intelligence (AI) can be used to augment massive event data processing technology in order to develop more intelligence detection and alerting tactics. The Azure Sentinel service from Microsoft is an example of a cloud-based, machine learning, and AI-focused SIEM.
(Also Read: Advantages of AI in Cyber Security)
For financial institutions and insurers, fraud detection necessitates a massive number of inputs and data types, as well as numerous intensive types of processing.
Cloud AI and machine learning engines, when combined with predictive models at scale, could aid in text mining, database searches, social network analysis, and anomaly detection.
Challenges leading to Financial Frauds
This could be extended to fraudulent cloud service use, such as an Office 365-based phishing attack from a hijacked account.
(Related: AI in Fraud Detection)
AI and machine learning models can process and evaluate data linked to labor operations in high-risk situations where mishaps can be severe or fatal. Before an accident occurs, AI systems may assess behavioral patterns and run prediction scenarios to enhance safety processes and prevent mishaps.
AI-based cloud analysis engines can categorize and tag all data uploaded and generated in a cloud environment based on established regulations, and then monitor for access, all based on recognized content categories and trends. Amazon Macie is one example of a service that employs artificial intelligence for this purpose.
Despite these advantages, there are two main disadvantages to incorporating AI into risk management procedures and practices.
The first is the financial aspect. Even with cloud-native services, processing huge amounts of data may be costly. Enabling specialized AI services will be expensive.
The second issue is privacy. With AI and machine intelligence, many in the security sector are concerned about data privacy. Data protection measures such as encryption, transit security, tokenization, and obfuscation may be required for data companies that upload to cloud services.
While most major cloud storage services provide data controls, specialized AI and machine learning services such as Amazon SageMaker; Amazon Rekognition, which uses AI to extract and analyze images and video; Azure Machine Learning and Azure Cognitive Services; and Google Cloud AI change this dramatically.
Because not all services can utilize the same encryption key management and usage methods and restrictions that businesses have in place, data may be exposed.
Apart from the services in use, the location of sensitive data utilized in machine learning and AI operations is a key regulatory and compliance concern.
(Also Read: Big Data in FinTech)
Watch this video on “AI Governance and Risk Management” from Google:
Risk management teams will continue to gain from the quick analytics processing of big data sets as cloud-based AI and machine learning services become more prevalent, reducing many of the constraints of more manual risk management and risk analysis procedures in the past.
There are some challenges, of course. Aside from the technical obstacles of developing AI apps for banking, such as designing proper and appropriate algorithms, there are also regulatory and data access rights issues to consider.
The fintech industry is controlled by a set of data-related laws that must be followed to the letter. Data breaches are expensive, and new law, such as the GDPR in the European Union, places a severe obligation on firms that handle personal data.
Read this document from TCS on “The State of AI in Risk Management”
The financial risk landscape is rapidly evolving. It takes a Herculean effort to stay on top of growing fraud risks, credit risks, and fast regulatory changes.
AI can help detect fraud and credit risk with greater precision and scale by augmenting human intelligence with extensive analytics and pattern prediction skills.
In the tech world, AI-powered analytics solutions may dramatically speed up compliance procedures while also lowering expenses.
6 Major Branches of Artificial Intelligence (AI)READ MORE
Reliance Jio and JioMart: Marketing Strategy, SWOT Analysis, and Working EcosystemREAD MORE
8 Most Popular Business Analysis Techniques used by Business AnalystREAD MORE
Top 10 Big Data TechnologiesREAD MORE
Elasticity of Demand and its TypesREAD MORE
What is PESTLE Analysis? Everything you need to know about itREAD MORE
An Overview of Descriptive AnalysisREAD MORE
5 Factors Affecting the Price Elasticity of Demand (PED)READ MORE
Dijkstra’s Algorithm: The Shortest Path AlgorithmREAD MORE
What Are Recommendation Systems in Machine Learning?READ MORE