Prof. D MukhopadhyayThe rise of Byjus, an Indian edtech unicorn, had been a remarkable success story in recent years. Founded in 2011 by Byju Raveendran, the company quickly gained popularity with its innovative approach to online education, attracting millions of users and securing substantial investments from prominent venture capitalists. However, beneath its glossy exterior, Byjus journey is marred by allegations of corporate governance fiasco , which eventually led to its downfall. This write-up explores the proximate causes of the rise and fall of Byjus, and sheds light on the recent hue and cry leading to Indias most eye-catching valuable startup with a valuation of $22 billion i.e about INR 1,8 lakh crore defaulted in servicing a debt of $1.2 billion i.e about INR 10,000 crore. Byjus began as a modest startup, offering video-based educational content to students across India. The companys early success could be attributed to its adaptive learning technology and engaging teaching methods, which resonated with a growing market of tech-savvy students. Byjus managed to secure significant investments from prominent investors, such as Chan Zuckerberg Initiative and Sequoia Capital, enabling its expansion and aggressive marketing campaigns. As a result, the company quickly became a household name in the Indian education sector, boasting millions of users and a valuation of billions of dollars.Despite its soaring success, Byjus journey was marred by various instances of corporate governance failures stymied by opaque accountability and transparency in the companys financial reporting.. Multiple reports and investigations revealed that Byjus indulged in unethical accounting practices, such as , inflating its operational revenue to attract more investments and such fraudulent activities eroded the trust of investors and raised serious concerns about the companys integrity. Byjus faced allegations of mishandling sensitive users information, breaching privacy regulations and engaging in aggressive data mining practices. These revelations not only damaged the companys reputation but also triggered legal action and regulatory scrutiny. The lack of data protection measures and transparency further exposed the weak corporate governance framework within the organization.Delioitte , Byjus Auditor, resigned on the ground that startup had not provided its financials over a year. Besides, three independent directors representing investors and lenders also resigned . The firm has been subject to lawsuits by the investors. It failed to honour the financial covenants and interestingly, Byjus filed counter lawsuits against the creditors and lenders. There has been a considerable number of layoffs and the prevalence of utter-chaos in the corporate governance mechanism which rocked the corporate world. The fiasco reminded the importance of robust corporate governance and the role of independent corporate governance professionals Chartered Accountants (CAs), Cost and Management Accountants(CMAs) and Company Secretaries(CSs) besides the auditors who are crucial in guiding the corporate governance and strategic management of the firm by signaling appropriate warnings for potential derailment and guiding the strategic management in earning investors confidence and trust. As far as auditors are concerned, they assess the accuracy and reliability of financial statements and ensure compliance with the applicable accounting standards, provide an independent and objective evaluation of a companys financial records, internal controls, and financial reporting system. In the case of Byjus, auditors are responsible for conducting thorough audits, including examining the companys financial statements, verifying revenue recognition practices and assessing the authenticity of financial data and confidence of investors in the financial transparency and accuracy of the companys disclosures.Chartered Accountants (CAs) specialize in various aspects of financial accounting, corporate finance, and taxation and they could play a significant role in corporate governance affairs by providing expert advice on financial management, compliance with accounting standards, risk assessment and adherence to ethical accounting practices.Similarly, Cost & Management Accountants(CMAs) have a critical role in keeping the company on track and maintaining the momentum in securing strategic performance , monitoring, evaluating and reporting the periodic results . CMAs specialize in strategic management accounting, analyses and interpretations of financial information for management decision-making. Their expertise lies in cost management, beyond-budgeting, strategic performance evaluation, and financial planning. In the case of Byjus, CMAs could contribute to effective corporate governance by providing insights into cost efficiency, profitability analysis, enterprise risk management, assisting investors gauge the companys financial health and its ability to generate sustainable returns. Finally, Company Secretaries (CSs) play a significant role in ensuring compliance with legal and regulatory requirements, as well as facilitating effective corporate governance practices.They are responsible for maintaining statutory records, facilitating board meetings, ensuring compliance with company laws, and advising on corporate governance matters periodically. In the case of Byjus, CSs would ensure the companys compliance with relevant laws and regulations, facilitate transparency, maintain a strong corporate governance framework and contribute to building investors confidence in the companys operations. Further, many researchers examined the relationship between corporate governance practices and stock market returns of publicly traded firms and their findings revealed a negative correlation between weak corporate governance and stock performance, indicating that investors tend to lose confidence in companies with governance issues, which has exactly happened in Byjus.To overcome the challenges and regain stakeholders trust, Byjus should consider implementing the recommendations for strengthening corporate governance practices to ensure transparency, accountability, and adherence to ethical standards. This can be achieved by appointing independent directors with expertise in corporate governance. These directors can provide unbiased oversight and ensure that the company operates in the best interests of its stakeholders. Stringent internal controls, robust financial reporting mechanisms, and regular audits can restore confidence in its financial operations, regain trust of the investors. Byjus should prioritize transparency and disclosure of accurate and reliable information to investors, regulators, and the public. Byjus must adhere to accounting standards and present financial statements reflecting manipulated financial performance. Transparent reporting and disclosure practices would help rebuild credibility and foster trust among stakeholders, besides , strengthening data protection measures. The company should strictly comply with relevant data protection regulations and standards, such as the General Data Protection Regulation (GDPR) and the Personal Data Protection system in order to assure users that their personal information is safe and secure, which will not only help rebuild trust but also mitigate legal and regulatory risks besides, fostering a culture of ethical conduct within strong ethical framework in the organization.Byjus should create an environment that encourages employees to act ethically and report any violations and non-compliance they witness.To actively engage with the stakeholders, including investors, customers, employees, and regulators, establishing open lines of communication, providing updates on corrective measures, and gathering feedback to improve its operations are urgently called for.The company should develop a robust risk management framework to identify and mitigate potential risks, including conducting regular risk assessments, implementing risk mitigation strategies, and monitoring risk exposure. Byjus should proactively collaborate with regulatory authorities such as the Securities Exchange Board of India(SEBI), the Ministry of Corporate Affairs(MCA), and other relevant bodies and demonstrate its willingness to cooperate and adhere to regulatory requirements in order to rebuild credibility and foster a positive relationship with the authorities overseeing corporate governance practices.As far as investors are concerned, in the wake of Byjus fiasco and to seek justice and mitigate credit risk, they are recommended to protect their interests by consulting with legal experts specializing in corporate law, securities law, and consumer protection laws in securing their rights and the available legal remedies which may include filing lawsuits against Byjus for fraud, misrepresentation, or breach of fiduciary duties. Investors and creditors should actively engage with regulatory authorities such as SEBI , MCA and other appropriate authorities to ensure appropriate action against Byjus. Besides, creditors should individually consider a robust credit risk management practice involving due diligence on potential borrowers, assessing their financial health, and monitoring their performance regularly by professionals, besides diversifying their portfolios and avoiding excessive exposure to a single borrower or company in order to reduce the impact of any individual default or credit unworthiness.Investors and creditors can also explore options for obtaining compensation through negotiations with Byjus. The companys management, board and legal representatives should seek restitution or settlement. By presenting a strong case backed by evidence and highlighting the impact of Byjus fraudulent activities, investors and creditors may have a chance to recover some of their losses through negotiated settlements or financial restitution. Moreover, investors and creditors should stay informed about the ongoing developments related to Byjus and its legal proceedings. The rise and fall of Byjus serve as a cautionary tale, highlighting the dire consequences of corporate governance failures over again and again starting,,, with M/s Satyam Computers or dates back Harshad Mehtas fraudulent activities and financial scams once severely rocked the Indian Capital Markets which have all circumstantially been evidencing inefficient and ineffectiveness of the existing regulatory mechasim reactive in nature and many a time fail to maintain their proactive role as the watch dog of the corporate sector. Despite its initial success, Byjus unethical practices, lack of transparencyand disregard for data privacy ultimately led to its downfall. It may not necessitate any special attestation to the corporate governance failure at Byjus, underscoring the importance of a robust governance framework in any organization . This serves as a reminder for both investors and companies to prioritize integrity, transparency, and accountability to foster sustainable growth and maintain public confidence and trust. Overcoming the challenges faced by Byjus requires a comprehensive approach that prioritizes integrity, transparency, and accountability and the investors and lenders are advised to have constant vigilance over the commercial operation and flow of authentic and reliable information . It may not be too much to say that critical success factors, including risks, need to be identified and investors should do their homework before deciding to park their investments in any portfolio, keeping in view the spirit of the maxim- Caveat Emptor .(The author is a Bangalore-based Educationist and Management Scientist)

Read the original here:

Rise and fall of Byju's A Corporate Governance Failure - Daily Excelsior

The advances in the field of Machine Learning have revolutionised other fields as well. With the increasing computational power and decreasing costs, the predictive power of modern-day deep learning networks allows scientists to apply those methods to various tasks that would be impossible to solve otherwise. Those advances did not omit the genomics field as well1,2. The first attempts to predict the expression solely on the DNA sequence started just after The Human Genome Project3however, they had a vast number of limitations4,5 and have mainly concentrated on the classical modelling approaches. However, those limitations started to disappear with the expansion of deep learning models. One of the first major studies on the usage of CNNs6 and XGBoost7 started a new era in predicting the expression with the introduction of ExPecto1. Then it continued with the use of CNNs through multiple models, including Basenji28, and finally with the use of transformer-based models like Enformer2. However, in our study, we have decided to take a standard approach available with the help of CNNs and expand it further with the input change to include spatial genomic information. The ExPecto model we decided to advance takes 20kbp surrounding the TSS of a given gene and uses expression from that to train a deep neural network to predict the epigenetic factors. Using those factors the tissue-specific gene expression profile is calculated with a high Spearman correlation score. In our study, we have investigated if the epigenetics marks alone are sufficient for the complex task of prediction of the expressionand have given a hypothesis that while they are incredibly informative, there is still a place for improvement. We decided that we would like to investigate the effects of the spatial chromatin architecture inside cell nuclei on the expression by exploring the models created with 3D information available and without it. To do that, we have modified the ExPecto algorithm accordingly, so it uses not only the 20kbp region around the TSS but also regions that are linearly distalbut are, in fact, spatially close, thanks to the spatial interactions that are mediated by specific proteins of interest. The overview of the algorithm proposed by us, SpEx (Spatial Gene Expression), is shown in Fig.1.

The architecture of SpEx. The spatial heatmaps are used for obtaining the regions close to the TSS (excluding+20kbp from TSS), and sequence from those regions is taken, and put into classic deep learning ExPecto modelwhich generates epigenetic signal over those regions. The classical features from ExPecto are merged with those obtained from spatially close regions, and the decision trees predict the expression levels. See Methods for more information about the algorithm.

To prove the model's validity, we decided to create an empirical study on how specific protein-mediated interactions are helping in the prediction of gene expression. To do that, we have selected the three most important proteins for loop creationcohesin, CTCF, and RNAPOL2. The effects of those proteins being unable to bind or be created properly were shown in multiple studies and were the inspiration for asking whether the machine learning models, provided we add 3D information (from interactions mediated by those proteins), will improve.

Cohesin is a protein complex discovered in 19979,10 by two separate groups of scientists. The complex is made out of SMC1, SMC3, RAD21, and SCC3. However, in human cell lines, SCC3 (present in yeast) is replaced by its paraloguesSA111, SA212, and SA313. However, SA3 appears only in cohesin during mitosis14, and we will concentrate on SA1 and SA2 since they are forming cohesin in somatic cells. The complex is essential in the proper functioning of the cell nucleusas is fundamental for the loop extrusion15, it stabilises the topologically associating domains (cohesin-SA1)16, allows interactions between enhancers and promoters (cohesin-SA2)16. The depletion of cohesin in a nucleus removes all the domains17, and completely destroys the spatial organisation of the chromatin. Mutations of cohesin negatively affect the expression of the genese.g. in Cornelia de Lange syndrome18,19 and cancer20, where the altered complex is incapable of sustaining its proper function, leading to diseases.

CTCF (CCCTC-binding factor) is an 11-zinc finger protein. Its primary function is the organisation of the 3D landscape of the genome21. This regulation includes: creating topologically associated domains (TADs)22,23,24, loop extrusion25, and alternative splicing26. The protein very often works with the previously mentioned cohesin complex, allowing loop formation. CTCF, as a regulator of the genome, binds to specific binding motifs and regulates around that loci. That is why, in case of mutations in the motifs, it might bind improperly, thus allowing disease development. However, not only mutations in the binding sites are disease prone. Mutations in the CTCF protein itself have proven to significantly influence the development of multiple conditions. Some of the examples of diseases induced by a mutation in the CTCF proteins include MSI-positive endometrial cancers27, breast cancers28,29, and head or neck cancer30.

Therere are three common RNA Polymerase complex proteins in eukaryotic organismsI, II, and III31. In this study, we will focus mainly on RNAPOL2, as that is responsible for the transcription of the DNA into messenger RNA32,33, thus having the most significant impact on the expression of the genes. The mechanisms responsible for creating the RNAPOL2 loops are complex and require not only RNAPOL2 protein but also several other transcription factors34,35. The mutations in those transcription factors have been shown to be linked to various diseases36, including acute myeloid leukaemia37,38,39, Von HippelLindau disease40,41, sporadic cerebellar hemangioblastomas42, benign mesenchymal tumours43, xeroderma pigmentosum, Cockayne syndrome, trichothiodystrophy44, and Rubenstein-Taybi syndrome45.

Multiple studies have shown the spatial landscape created by cohesin-mediated chromatin loops. The first major cohesin ChIA-PET study from 201446 showed the internal organisation of chromatin in the chromosomes. For example, the study provided a list of enhancer-promoter interactions, which can be a starting point for gene expression study.

The next study from 202047 extended the 2014 study and showed that among 24 human cell types, 72% of those loops are the same; however, the remaining 28% are correlated to the gene expression in different cell lines. Those loops mostly connect enhancers to the promoters, thus regulating the gene expression. Another interesting insight from this study is that those different profiles of interactions are effective in clustering the cell types depending on the tissue they were taken from.

CTCF, as mentioned above, is responsible for loop extrusion. That is why it is very popular to investigate CTCF-mediated interactions. Once again, like with the cohesin complexes, ChIA-PET is used for obtaining the interactions mediated by CTCF. One of the major studies from 201548 shows the genomic landscape among 4 cell lines. They discovered that SNPs occurring in the motif of the CTCF-binding site can alter the existence of the loopand by that, contribute towards the disease development. They assessed the SNPs residing in the core CTCF motifs and found 70 of those SNPs. Of those, 32 were available from the previously done GWAS studies, and 8 were strongly associated (via linkage disequilibrium) with disease development.

Another study from 201949 analysed mutations using 1962 WGS data with 21 different cancer types. Such an analysis, enhanced with the usage of CTCF ChIA-PET data, showed that disruptions of the insulators (that are creating the domains) by motif mutations and improper binding of CTCF (and, by that, diminish of the loop) lead to cancer development. Using a computational approach, they have found 21 potentially cancerous insulators.

The transcription chromatin interactions, such as the ones mediated by RNAPOL2, are of great interest as wellthey control the transcription directly, after all. The study from 201250 showed the RNAPOL2-mediated ChIA-PET interactions on 5 different cell lines to show the transcriptional genomic landscape. Another study from 202051 performed the same experiments on RWPE-1, LNCaP, VCaP, and DU145 cancer cell lines. Similar to the 2012 study, they have shown the spatial interactions based on RNAPOL2, but this time in cancer cell lines. Furthermore, they showed that cohesin and CTCF interactions provide a stable structural framework for the RNAPOL2 interactions to regulate the expression, thus making all of the proteins that we describe in this section crucial for the proper expression of the genes.

Those findings were the main motivation for our analysisas based on the evidence, the cohesin, CTCF, and RNAPOL2 interactions should give us more information on the genetic expression, thus improving the metrics for the machine learning models. In this work, we present an extension of the ExPecto1 deep learning model that is enriched with spatial information, thus, as expected, improving the statistical metrics.

ExPecto1 is a model introduced in 2018 for predicting gene expression from the sequence. It uses a deep neural network (namely, Convolutional Neural NetworkCNN). It is composed of, most importantly, 6 convolutional layers, 2 MaxPoolings (the activation function for all the layers is ReLU). For the exact architecture, see the original paper. As mentioned, the input to the network is the DNA sequence, and the output is in the form of the 2002 epigenetic factorscollected from ENCODE and Roadmap Epigenomics. The network takes 2000bp as the window and predicts the epigenomic of its 200bp middle, using the remaining base pairs as the context. The model is then applied to 20,000bp region surrounding TSS, and the step size is determined by the aforementioned 200bp, yielding 2002 features multiplied by 200 bins (100 left and 100 right), so the total number of features describing the gene is 400,400. Then, those features are transformed using exponential functions (10 upstream and 10 downstream TSS), so the final number of the features is 40,040. Then, xgboost (namely, gradient boosting of linear regression models) is used for the prediction of the expression of gene expression. They obtained a Spearman correlation score of 0.819, and the testing was done on chromosome 8.

View post:

Enhanced performance of gene expression predictive models with ... - Nature.com

DATA MINING DISCUSSION - Justice of the Peace Donnie Herring (left) of talks to County Attorney Tiffany Nutt about some of the issues surrounding an ordinance that establishes building guidelines for data mining centers in Cleveland County. Herring, along with Justice Charles Rodgers of Rison, were both critical of the centers. Herring cited the fact that the centers are a tremendous drain on a community's electrical infrastructure and the centers do not provide any tax or employment benefits.

RISON - The Cleveland County Quorum Court and the Rison City Council both approved ordinances this week placing noise limitations and other restrictions on data mining centers that might come to the county or city.Data mining centers are facilities that house large banks of computers that run complex code for cryptocurrency like Bitcoin or process large amounts of digital data for businesses. Most data mining centers house thousands of computers which require a tremendous amount of electricity and also create high levels of noise to keep the computers cool.The ordinances approved by the Cleveland County Quorum Court and Rison City Council are essentially identical. County Judge Jimmy Cummings said the model ordinance was put together by a legal team with the Association of Arkansas Counties.The ordinance requires the owner of the property where the data center will be located to notify the neighbors of the intent to place a center there. It also requires the property owner to conduct noise level tests along the boundary of the property both before and after the center is built. There are also stipulations regarding what noise-reducing measures that must be included in the building plans.The complete ordinance can be found in its entirety in our public notices database, or in the printed edition.Local governments have until Aug. 1 to approve such guidelines before Act 851, a new state law governing data mining centers, goes into effect.Data mining centers have become a hot topic for local governments this year after residents living around some of the centers that have already been established in Arkansas and other states have complained about the loud noises coming from the facilities.Most data mining centers house thousands of computers. With that many computers working at the same time, a great amount of heat is created that must be pulled from the building to allow the computers to function properly. Currently, the most common method to do that is

Here is the original post:

County, Rison Both Approve Ordinances on Data Mining - Cleveland County Herald

The results of the demographic characteristics of the study and the average difference in depression levels are shown in Table 2. Females accounted for a higher number than males; females (n=2008, 67.5%) and males (n=967, 32.5%). For the age distribution, ages of 80 or older was the largest with 1475 people (49.6%), followed by ages of 7579 with 755 people (25.4%) and ages of 7074 with 459 people (15.4%). Regarding the level of education, 2107 people (70.8%) had elementary school graduation, and 471 people (15.8%) had middle school graduation. indicating that the majority had a low level of education. When asked about the number of household members, two people accounted for the largest portion with 1459 people (49.0%), showing that the majority lived with one more person. As for having disabilities, 2425 people (81.5%) mentioned no, and 550 people (18.5%) stated yes. In terms of participation in economic activities, not participating accounted for more than half of the participants (n=2042, 68.6%).

In terms of depression, 2553 people (85.8%) reported no and 422 people (14.2%) reported yes. Lastly, the average difference between the sociodemographic characteristics and the depression level of the participants was evaluated. As a result, there were significant gender differences (t=3.547, p<0.001) and participation in economic activities (F=7.326, p<0.001), but no differences were found in other factors.

The descriptive statistical results of the main factors are shown in Table 3. Considering that the range of scores for health promoting behavior is from a minimum of 0 to a maximum of 3, an average of 2.2 points can be regarded as a high value. On the other hand, having a standard deviation of 0.72, it can be understood that there was no significant difference in health promotion behavior by the elderly in low-income households. With reference to subjective health awareness, it was found that numerous elderly people had a higher awareness than the average, with an average of 2.8 points. Regarding the level of medical expenses, it was found that the average monthly expenditure was 158,000 won, and the standard deviation was 20.17, indicating a high difference in expenditure among the elderly in low-income households. Family support, social support, and leisure life satisfaction showed average scores of 2.7, 2.6, and 2.3, respectively, which were verified to be in good standing, considering that the range of scores was at least 0 to up to 4.

The relative importance of the predictive factors that contributed to predicting depression in low-income seniors utilizing the feature selection, is shown in Table 4. The higher the order of importance of a predictor, the greater the influence of that factor in predicting the level of depression; the highest ranking was identified as 'leisure life satisfaction.' This result can be interpreted as having the greatest effect on satisfaction in leisure life than other factors when predicting the level of depression of the elderly in low-income households. Furthermore, the factors of subjective health awareness, family support, and social support were found to be in the upper ranks. However, it was noted that the factors of presence or absence of chronic diseases, educational level, disability, and health behavior were distributed in the low ranking. A SHAP summary plot was created (Fig.2), a visualization of how much each explanatory variable affects the prediction of depression. A yellow bar indicates a positive influence on the occurrence of depression. The red and orange bars indicate a negative impact on the occurrence of depression. The red bars were found to be the most influential variables. Regarding leisure life satisfaction, it can be used as an explanatory or a dependent variable. This study used it as an explanatory variable because the subjects were low-income elderly. The relationship between leisure life satisfaction and depression in low-income elderly is often reported as causal, with leisure life satisfaction affecting depression29.

The summary plot of the SHAP values.

In this study, the classification techniques used to develop the most accurate predictive model, predicting the level of depression of the elderly in low-income households, were artificial neural networks, decision trees, logistic regression and random forest analysis. Table 5 is the result of the classification analysis by sequentially applying the wrapper's stepwise method to the relative importance of the factors identified in Table 4. Based on the analysis, it was identified that the decision tree algorithm showed higher predictive power than the other three algorithms. In the case of logistic regression analysis, the prediction accuracy was 73.2%, and the artificial neural network showed 81.8%. On the other hand, the decision tree shows a tendency to increase predictive accuracy as the number of factors increases, except when there is only one input factor. When all 13 factors were input, an accuracy of 97.3%, a sensitivity of 100%, and a specificity of 94.6% were presented. Finally, when forming the decision-making tree, the factor that had the greatest impact was the subjective health awareness factor, followed by leisure life satisfaction, family support, and social support. To ensure that the main outcome was reliable and robust, a sensitivity analysis was conductedby dividing the dependent variable, depression incidence, into two thresholds (15 points or less, 16 points or more); the analysis revealed that the main outcome did not change in Tables 6, 7.

Logistic regression analysis was performed to seek the influence of the predictors of high risk of depression in the elderly from low-income households, and the results are shown in Table 8. The factors that affected the level of depression were gender, number of household members, subjective health awareness, family support, social support, and satisfaction with leisure life. In the case of gender, the probability of developing depression in women was confirmed to be 1.86 times (OR=1.861, 95% CI=1.1732.954) higher than in men. As the number of household members increased by each level, the probability of depression decreased by 0.69 times (OR=0.692, 95% CI=0.5130.933). In subjective health awareness, an increase of each level was associated with a 0.40-fold (OR=0.403, 95% CI=0.3120.522) lower probability of depression. Further, family support (OR=0.613, 95% CI=0.4940.759), social support (OR=0.711, 95% CI=0.5520.916), and leisure life satisfaction (OR=0.425, 95% CI=0.3280.425) showed that the probability of depression decreased by 0.61 times, 0.71 times, and 0.42 times, respectively, as the level increased by each level.

Here is the original post:

Development of a prediction model for the depression level of the ... - Nature.com

Alpha enables financial services firms to get instant answers utilizing next-generation large language models.

NEW YORK, July 12, 2023 /PRNewswire/ --Alkymi, the leading business system for unstructured data, today announced the launch of Alpha, a generative AI-powered solution that allows financial services teams to uncover instant answers from documents and proprietary data sets through an interactive chat experience and structured workflows. Using Alpha, customers will be able to accelerate critical business activities such as conducting due diligence, interpreting investment performance, crafting quarterly reports, as well as answering client inquiries.

Until now there has been no way to securely apply large language models (LLMs) to targeted documents and data sets in financial services with a turnkey workflow. Due to these concerns, financial services firms have had to sacrifice access to insights that could have a major impact on business outcomes.

Alkymi has solved this challenge by allowing firms to create their own unique, private, and secure data sets from over 25 supported file formats for data mining and insight generation. Alpha helps customers systematically generate answers from these document data sets in a user friendly way. Alkymi is empowering customers to use LLMs with confidence, keeping their data encrypted and ensuring it is not used to train a model. To build confidence in model output, answers can be traced directly back to their sources in documents.

Customers get insights instantly and can continue to drill down for more context where needed while being assured of the security of their proprietary data. Alpha features include:

"One of the key obstacles that financial services firms face when adopting generative AI is the lack of control - how you apply the technology safely and transparently," says Harald Collet, CEO of Alkymi. "We built Alpha to remove these obstacles through an industry-focused user experience that empowers customers to capture the massive opportunity of generative AI."

"We see tremendous potential in using LLM-powered tools," says Robin Williams, Managing Director of Asset Vantage. "Alkymi's focus on financial services use cases makes Alpha particularly exciting because they understand the business outcomes clients like us need, including the security and governance requirements of financial services firms and their data."

Alpha by Alkymi enables deeper discovery and empowers customers to generate insights, all within a secure environment and with full traceability. It is currently widely available and more information can be found at alkymi.io/product/alpha.

About AlkymiAlkymi is the business system for financial services firms to unlock their unstructured data utilizing the most advanced machine learning and AI technology. Alkymi helps leading firms like Interactive Brokers, SimCorp, and Strategic Investment Group accelerate decision making by giving them the tools to understand, transform and leverage critical data found in emails and documents. For more information, visit http://www.alkymi.io.

Media ContactBethany Walsh[emailprotected]

SOURCE Alkymi

More:

Alkymi launches Alpha, a Generative AI-powered solution built for ... - PR Newswire