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Using artificial intelligence for forensic probe

March 31, 2021
at AI has helped in figuring out the spread of the virus worldwide, and thus measures were taken accordingly. In cancer treatment, the involvement of AI is beyond expectation. Artificial Intelligence has been implemented for the development of a neoantigen vaccine or blood test-based cancer diagnosis. The AI algorithm used for diagnosing breast cancer gives a much accurate and quick report than pathological analysis. 2. Cloud and AI collaboration In 2021, Artificial Intelligence will be playing a pivotal role in the adoption of cloud-based solutions. Rico Burnett, the Global Director of Innovation at Exigent Group Limited, says that through the collaboration it will become very easy to monitor and assess cloud resources and a huge amount of data available there. 3. Artificial Intelligence in the IT industry IT industry will be highly influenced with the help of AI technology, leading technology giant Capgemini, predicts. AI will lower down the complexity in projects by its self-detecting and error-handling features, which will eventually lead to a significant impact on the workforce to speed up the work and focus on other niches. The AI and ML models will also enlarge the production scale and get ROI in real-time. 4. AI and cyber-security The latest trend of Artificial Intelligence has shown promising results in cybercrime, especially AIOps. The technology hinders hackers to perform malicious activities, expose the data and breach the cyber-security. An AI-based cyber-security framework can secure IT asset inventory, making the prediction of infringement and maintaining an effective security stance. On top of all these, AI technologies respond to security warnings, respond to it, figure out the root cause, and tighten the system to evade future concerns. 5. Autonomous e-vehicles Tesla will face a significant challenge as Chinese company NIO has already introduced their cheaper electric vehicle in the market. The Japanese automobile companies like Toyota and Nissan are in the pipeline to launch their AI technology-driven vehicles having similar features like ‘Autopilot by Tesla’. 6. AI transforming businesses In the retail sector, education sector, and other diversified business portfolios, AI will be bringing a lot of advancement to boost up the growth of the organizations. Business giants like Amazon, Walmart, Tesco, etc. are using the real-time application of AI for product forecasting, prediction of consumer behaviour, supply chain management, marketing assessment, etc. 7. Conversational AI Conversational AI or the AI- powered chatbots, also tops the chart of the AI trends in the current year. The Conversational AI is mostly used for enhanced customer experience, reach out to target audience, have improved personalization and responsiveness. It has enabled corporates to have a completely automated customer service. 8. Quantum AI Quantum AI is in vogue lately, where companies are using quantum supremacy to measure the Qubits for use in supercomputers. Quantum AI helps in pacing up the problem-solving techniques compared to the basic computers and will also help in predicting unique trends and interpret data much faster. 9. Edge Computing Edge computing provides gadgets with servers and data storage facilities. They are able to access devices and feed data into them. It can be defined as data processing in real-time which is faster than any cloud-computing service. It has a lower cost, saves time and provides faster service to the customers. 10. Confluence of AI and IoT Amalgamation of IoT and AI is seeing an upsurge. Both technologies, in spite of having completely different traits and characteristics are often seen to yield impressive results when used together. They open up better prospects and possibilities together. Alexa and Siri, our intelligent voice assistants have already displayed how effective the confluence of these two technologies can be. Needless to say, AI with IoT will be a game-changer within a few years and a solution for 80% of businesses all over the world. There are hundreds of other AI trends buzzing around us, wondering how to integrate it for NLP, reinforcement learning, quantum AI and so on.

Using artificial intelligence for forensic probe

With evolving business models, increased use of tech and a changing regulatory landscape, fraud management is fraught with newer and more complex challenges. These are further exacerbated during cross-border investigations, where varied levels of standardisation, languages, local laws and regulations, along with specific cultural attributes, bring additional complexities—mandating an investigation methodology standardisation and requiring tools for quick insights.

While cross-border investigations could relate to bribery and corruption, FCPA, embezzlement, conflict of interest, data breach, IP theft, financial reporting fraud, etc, these investigations often entail the following procedures:
—Drawing inferences by correlating external intelligence (open source information, adverse media, subscription services) and internal intelligence (business operations, transactional data, employees and vendors)

Extracting information from varied documents and formats in a timely manner;

—Establishing a relationship between key entities and individuals;

—Understanding event chronology, specifically on information exchange using mediums such as, but not limited to, emails, SMS and other messaging platforms, call records and social media communications; and

—Understanding processes and generating insights from transactional data.

Emerging technology and artificial intelligence (AI) can help make investigations efficient, generate insights, and/or aid reviews. Optimal use of AI demands the knowledge of ‘possibilities and limitations’ of such techniques, either in the form of ‘special purpose software’ or the ability to combine various methods. While there are numerous areas for AI application, some solutions/techniques worth mentioning are:

—Identification of relevant content: Embedded AI in specific e-discovery and data analytics platforms helps narrow down content by generating interactive document visualisations and classifying them. Semantic search automatically identifies related concepts and documents by training the tool using select documents and reducing noise;

—Digitisation, data extraction and automation: Advanced computer vision algorithms offer the ability to extract information from bank statements, invoices, waybills and various other documents, and are often combined with natural language processing (NLP) techniques to extract entities from documents and perform automated verification using digital channels. Verification of tax details, KYC documents, incorporation details, etc, may be expedited using a combination of AI techniques;

—Advanced analytics: These have extensive applications in investigations, including:

1. Sentiment analysis: It can differentiate documents based on tone, subject, positivity or negativity, and helps identify suspicious conversations, reviews, news articles, etc.

2. Entity extraction: AI can help discover additional undisclosed entities referenced in documents, emails, bank statements, etc, by applying entity extraction methods.

3. Network analysis: It’s a powerful medium for analysing information flow and connecting the dots in an investigation to help establish relationships between individuals and entities based on business dealings, reference information and reporting hierarchy.

4. Outlier scanning: Supervised and unsupervised learning algorithms can help detect anomalies and outliers from transactional data.

—Process analytics: Some recent applications of AI allow users to visualise a ‘metro-map’ of business processes, which can help link undesired process behaviour, and further scrutinise underlying users and transactions for non-compliance or violations.

As significant a role as these tools and technologies may play in the fight against fraud, use of AI, NLP and other technologies come with their own set of challenges. One must consider scalability, the ability to reproduce results consistently, and prior knowledge of these tools for their efficient use. Further, emotion and instinct remain exclusive to mankind and AI has not been successful in replicating this so far.

While cross-border collaborations and global settlements increase in frequency, technologies that reduce manpower and improve efficiencies become more relevant. With over 5% estimated revenues being lost to fraud each year (2020 Report to the Nations: Asia-Pacific Edition), and international crimes gaining complexity and chronicity, it’s time we equipped our investigators with the right AI tools and technologies to tackle these situations.

Source-Financial Express

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