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How AI Is Revolutionizing the Financial Industry

Artificial intelligence is quietly transforming the financial industry, streamlining processes, enhancing decision-making, and redefining customer experiences. You see its influence in everything from managing risk to personalizing financial advice. Previously time-consuming and error-prone, it lets institutions evaluate massive data sets. AI has improved financial services efficiency and precision, enabling faster and more informed choices. Businesses and customers gain from this transition, which lowers costs and improves services.

One of the most significant changes brought about by AI lies in fraud detection and risk management. Financial institutions constantly face risks from fraudsters who exploit security vulnerabilities. AI-powered systems recognize trends in real-time transactions and highlight anomalous activity that humans may miss. As they handle more data, these systems improve accuracy. AI rapidly reduces reaction times to detect fraud, save losses, and protect consumer assets.

In investment and trading, AI has redefined how people make decisions. Machine learning algorithms forecast market patterns from vast datasets with surprising accuracy. Thus, financial analysts use AI-driven technologies to improve their strategies and invest intelligently. This has made complex financial research more accessible, helping smaller enterprises and investors compete. AI-driven trading algorithms seize market opportunities in milliseconds.

The financial industry has also seen AI revolutionize customer interactions. Chatbots and virtual assistants, powered by natural language processing, have become common in handling customer queries. You might find yourself interacting with AI when seeking assistance from your bank. These systems offer quick, accurate responses, improving overall customer satisfaction. Furthermore, they allow human employees to focus on more complex tasks, balancing automated efficiency and human expertise. As AI becomes more refined, it can anticipate customer needs, providing tailored financial advice and solutions.

Loan approvals and credit assessments have benefited significantly from AI's capabilities. Traditional credit scoring systems relied on a narrow set of parameters, often excluding potential borrowers. AI changes this by incorporating alternative data sources, such as spending patterns and payment histories, to assess creditworthiness. This broader analysis creates opportunities for individuals and businesses that previously struggled to access credit. It also reduces biases because data, not subjective assessments, drives decisions.

AI plays a crucial role in regulatory compliance and reporting. Financial institutions operate under strict regulatory frameworks, requiring meticulous reporting to avoid penalties. AI simplifies this by automating compliance checks and ensuring institutions adhere to guidelines. By analyzing large datasets, AI tools can identify discrepancies or potential breaches early on, minimizing the risk of legal complications. This capability reduces manual labor and enhances accuracy, fostering greater trust in financial systems.

The insurance sector has also experienced a profound impact from AI adoption. Insurers now use AI tools to assess claims, evaluate risks, and set premiums more effectively. When you file a claim, AI systems can analyze the data, including images and documentation, to make faster decisions. This leads to a smoother claims process and reduces delays, improving the customer experience. Predictive analytics allows insurers to identify risks before they materialize, ensuring more robust financial planning for providers and policyholders.

Despite these advancements, implementing AI in finance comes with challenges. Issues surrounding transparency, ethical concerns, and regulatory compliance remain significant. You might wonder how decisions made by AI systems are justified, particularly in high-stakes scenarios. This "black box" problem—where AI outcomes are difficult to explain—raises questions about accountability. Addressing these challenges requires careful regulation and ongoing collaboration between technologists, regulators, and financial institutions.

AI expands financial services beyond operational efficiency, driving innovation in previously unattainable sectors. By integrating technology advances with ethical concerns and human oversight, the financial industry may enhance its systems and regain client trust in transparency and security. Working with AI rather than replacing it will create a more dynamic and inclusive financial landscape.

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How InVivo Biosystems Uses Humanized Models for Understanding Disease

InVivo Biosystems has become an industry expert in genome editing used in disease research. The biotechnology company offers drug toxicity and effectiveness testing and identifies drugs for repurposing for rare genetic diseases. Importantly, InVivo Biosystems uses humanized models as part of its work in understanding disease.

Traditional research has used rodent models to come to conclusions regarding the efficacy and safety of drugs used to treat various diseases. However, rodent models have shortcomings related to genetic drift, which refers to how genes change through evolution or mutation over time. The problem with genetic drift in rodent model studies is that it can culminate in researchers getting inconsistent results over time, making study results less reliable.

In conjunction with CRISPR gene editing research, humanized models offer researchers more consistent results in pre-clinical research, allowing InVivo clients (companies and researchers) to study human diseases accurately and effectively. Using humanized models enables researchers to understand the impact of gene variations in highly controlled environments, making it relatively simple for researchers to track genes and provide researchers with insights. The InVivo humanized model, for instance, enables researchers to get results quickly using transgenic technology and AI. Finally, humanized models allow researchers to conduct drug trials cost-effectively, enabling them to conduct studies that provide valuable data.

The company uses zebrafish and Caenorhabditis elegans, also C. elegans, humanized models to deliver results to researchers. In the last few years, the zebrafish (Danio rerio) has become a popular model for understanding and studying vertebrate gene function. The fish is a small one found in tropical freshwater environments in South Asia.

The fish lays eggs outside its body, simplifying modifying them for researchers. For instance, researchers can inject DNA or RNA into embryos to alter their genetic makeup, creating a knock-in in zebrafish used as part of CRISPIR Cas9 gene editing research. The embryos are also transparent, enabling researchers to observe growth and development, something not possible with mice.

While humans and zebrafish belong to entirely different families and species, 71 percent of human genes are also in zebrafish. Also, 84 percent of human genes linked with disease have a zebrafish counterpart. Researchers can use the latter to model diseases because humans and zebrafish share some of the same genes.

As mentioned above, researchers used a modified version of the zebrafish, called the knock-in in zebrafish, in conjunction with CRISPR Cas9 to study disease. Researchers modify the zebrafish DNA/RNA by using CRISPR to remove DNA/RNA at a location on the strand and replace a human gene in that location. Researchers in the Netherlands have already used zebrafish models to study cardiovascular disorder-causing genes. Researchers in Seattle have used zebrafish models to better understand congenital heart defects.

Like the zebrafish humanized model, the C. elegans also offers research benefits as a humanized model. C. elegans belongs to a class of worms (nematodes), such as roundworms and threadworms, found in soil. They are smooth-skinned and non-segmented worms with long cylindrical bodies tapered at both ends. They grow to about one millimeter in length and are not dangerous or parasitical.

It is surprising how two completely different organisms can have similar biological makeups, as with humans and C. elegans. The nematode is conceived as a single egg that cleaves upon conception. It also has a brain and central nervous system and can engage in some learning. It is similar to humans in its reproduction in producing sperm and eggs, mating, reproducing, aging, and dying. Nematode genes control all of these processes, making them appropriate for gene editing.

C. elegans, again using CRISPR technology, enables researchers to study rare diseases. This model is more effective than whole genome sequencing, which is only effective 25 percent of the time because of variation, according to a January 2023 Nature article. This model and CRISPR technology allow researchers to test gene variations inexpensively and quickly by replacing genes within the nematode with human genes.

This approach to disease study and drug discovery offers researchers an innovative, accurate approach to coming to trial conclusions. For more information on the InVivo method, please visit invivobiosystems.com/drug-discovery/humanized-models/.

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Subscription-Based Service Challenges

A subscription-based business model presents unique marketing challenges. In the traditional model, where one-time purchases prevail, companies need to convince a potential customer to purchase a service once. With a subscription, companies have to concern themselves with customer retention, in addition to the challenges and high-cost of acquisitions.

Subscription services range from consumer software like Netflix to business tools like Salesforce. One of the challenges of acquiring subscribers lies in communicating the value of paying a recurring fee. Companies can help overcome this by focusing on increasing lifetime value and communicating it well by switching consumer focus from ownership to providing a solution.

For example, a movie enthusiast motivated by ownership would need to purchase a DVD to watch a movie. For a consumer looking to watch movies on demand, the ownership route is inconvenient. A streaming service like Netflix offers convenience.

Companies can also offer demos and free trials. These build trust by eliminating the risk of committing to a service a customer isn’t sure works or is a good fit. Demos and free trials are also useful when a company is struggling to articulate the benefits of its subscription service and its value proposition.

Customer churn (subscribers canceling subscriptions) is another major challenge in the subscription business. Reasons customers unsubscribe include the enticing features, pricing, or functionality of competitors’ services. Churn can also be involuntary due to failed payment.

To reduce customer churn companies must deliver products or services that meet customer needs and expectations, thereby justifying the recurring payments. To remain attractive to users, companies must innovate and update features. Responsive customer care service also helps boost brand loyalty.

Another explanation for customer churn is that customer needs and preferences change. Once a user has solved their problem, there may no longer be a reason to stay subscribed to a service. They will cancel and move on to another service that solves their current problem. Companies should ensure the evolution of their products’ features reflects users’ ever-changing preferences, needs, and motivations through data-driven and customer-centered innovation.

Pricing and packaging services can also be challenging. Price is a major consideration in the subscription service. Customers want to pay a price commensurate with the value they’re getting. It’s not just about matching price with value or having the lowest price. Pricing and payment options must suit customers’ specific needs.

Companies must also offer convenient payment options for seamless subscriptions. Payment options should also reflect customers’ purchasing preferences. A company selling software globally should offer country-friendly payment options. Another strategy for making pricing flexible is offering different subscription plans, such as monthly, quarterly, annual, or on a per-user basis.

A subscription-based business model allows companies to serve users across continents without setting up physical offices in those countries. With this global reach, however, comes a host of legal and regulatory challenges. Take the EU, for example. It has the strictest data regulations, and companies operating in the EU must adhere to them. Other country-specific regulations to be aware of and adhere to include billing and taxation rules and reporting practices.

To stay compliant, companies should consult with legal and financial experts from the countries where they operate. They should also implement and maintain internal policies to ensure regulatory compliance, such as data collection and use policies.

For many customers, subscription is a set-and-forget affair. To stay top of mind and boost brand loyalty, brands should regularly engage customers. They should also consistently deliver on their promises and constantly innovate. This helps prevent churn while increasing the chances of being recommended by their loyal users, helping to reduce acquisition costs.

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How Publishing Has Leveraged AI

Publishing is the one industry where the human voice and thought are central to its existence. However, artificial intelligence (AI) is now a part of publishing, with some platforms allowing users to create AI-generated content, changing this voice. In addition to generating content, AI has other uses in publishing.

Recent advances in AI have enabled computers/machines to process and mimic human language. This advance in AI technology allowed businesses to connect with consumers using chatbots. However, this evolution of AI and language has fueled models that can analyze numerous pieces of literature, whether in books, images, or articles. Nature Publishing Group and PNAS Journals have revised their publishing guidelines to adapt to this evolution in AI-generated content.

Naturally, those in writing and editing were likely to be the first victims of a technology that offered consumers a way to create content easily. Some in the publishing industry became uneasy when OpenAI launched ChatGPT because they thought robots might replace them. One might understand how those who write and edit content might be concerned with a platform that amassed 100 million users and spurred additional investments in similar technologies.

However, AI technologies only complement writing and editing, which enabled these professionals to be more productive. Large language model technology can check grammar, provide content summaries, or format content for social media, scripts, podcasts, or videos. ChatGPT, specifically, can assist writers with creating headlines. For instance, News Corp Australia reported in July 2023 that staff members produced 3,000 articles using AI technologies in one week. At the same time, the news organization stated that the AI technologies used involved templated information, such as daily court lists and prices.

AI has also changed content distribution, assisting publishers with determining the optimum times, formats, and channels (social media) for disseminating content. Some publishers might base their decisions to distribute content on their readers/viewers' demographic characteristics or behaviors. For instance, WordStream and Emplifi are AI-assisted technologies that provide smart scheduling, campaign optimization, advanced tracking, audience data, and advertising.

AI has also simplified the creation of the visuals that accompany many of the articles published online. Authors and editors can create visuals for their written content using platforms like Midjourney, DALL-E, or Stable Diffusion. Depending on the platform, they can do it in as little as seven minutes. More importantly, the subscription costs for some of these platforms are inexpensive, making creating visuals accessible to everyone.

Other uses of AI in digital publishing are developing ideas, predicting emerging trends, and streamlining routine tasks. It can also help personalize emails as part of marketing.

In the end, AI offers writers, editors, publishers, and anyone in publishing more benefits than disadvantages. As with most AI-integrated industries, this technology allows users to streamline repetitive work, reduce work, and allow teams to spend time on idea creation and other parts of publishing. Regarding distribution, it will enable publishers to hone in on niche markets without spending a lot on advertising or marketing.

AI tools also help journalists digest large amounts of information across stories. To find engaging, relevant stories, writers can find patterns and connections between facts, events, entities, and people. Beyond story development, AI tools are also helpful in ferreting out fake information from fact because they can compare newly published articles against reliable sources or track the history of an image beginning when search engines first detected it.

In the beginning, this tool seemed to be one that might replace publishers, writers, and editors. Ultimately, they have leveraged it to be more productive while managing costs.