At Statswork Research & Analytics Consulting, our research team is fully immersed in the field of banking, finance and insurance. We have niche expertise to conduct research on financial services, digital transformation, banking insights and e-banking services that can only come from decades of hands-on experience.
Statswork has been a trusted partner in financial and banking services for decades, and when you choose us, you get the best-in-class quality with the highest accuracy services. We provide fully scalable service with security, privacy and at the same cost-effective pricing.
Industry & Market Research
Development of thought leadership work
Explore banking, insurance, financial services research needs in the area of online traffic communities, application of information technology in tourism, big data and analytics, digitalization, and many more when needed utilizing a number of sources and advanced secondary research techniques (desk research, interviews of SMEs, surveys).
Conduct in-depth research on geographic and/or horizontal segment trends, competitors, industry market trends and issues, and relevant technology.
Our expertise has an understanding of quantitative and qualitative research skills, with the ability to synthesize findings from case studies, analysis of survey data, regression analysis, expert interviews etc.
Our analyst team write reports, points of view, articles in journals blogs, develop insightful case studies, design questionnaire, analyse data, runs regressions and time series analysis.
We develop business Logic and train business models to achieve your business goals. We develop various deep learning and machine learning algorithms, including collaborative filtering, financial fraud detection methods, credit scoring applications, pattern/anomaly detection, automatic decision management to help automate your business.
Whether you need help Collecting customer or supplier or historical data or have an existing database, or user-generated content from social media, such as data from bank sites, Cheques, Forms from banks, multilingual data collection, handwriting data or natural language utterance data collection, our StatsWork team provide support in collecting any type of data.
Make your data meaningful – the process of data labelling and classification. We add meaning to data. Our audio, video, image, and text annotation services deliver the valuable information you need for building, training and testing your machine learning algorithms. With our subject matter expert, high-quality linguistic and semantic annotation solutions enable machine learning algorithms to perform better with accuracy.
We analyse the business requirement and then formulate a model process. Then we collect the data and make required changes such as data annotations, labelling and data processing.
Then we build a sophisticated machine learning or deep learning model on top of that data. We then analyse the result of the model and give a suitable solution that the client can roll out on the business strategy to increase the yearly ROI.
Presenting the results in a clear and logical format to the client is the most important task. It should be tailored to address the aims and objectives of the survey, and at the same time, consideration should be given to the level of statistical understanding (terminologies) of the clients and users. Statswork transform raw data into a visual story by offering readable and technically acceptable report that balance words, tables, maps, and graphs.
Back-testing is important because it is traditionally used to evaluate how well banks’ risk models are performing. Unsupervised learning algorithms help model validators in the ongoing monitoring of internal and regulatory stress testing models, as they can help determine whether those models are performing within acceptable tolerances or drifting from their original purpose
Bankruptcy fraud, application fraud behavioural and theft/counterfeit fraud.
We apply appropriate machine learning and deep learning algorithms, which can predict fraud when it happens during the transaction of money. The data was collected prior with a balance of fraud transactions and normal transactions so that the machine could learn and build a predictive model.
Profiling of clients based on their score. AI can automate the categorization of the client depending on their risk profile from low to high. Machine learning algorithms are trained on historical client data and pre-labelling data provided by the advisor, which eliminates data-induced bias.
We have built a credit scoring model using various machine learning algorithms as a cascading model. Through this credit scoring model, companies can assess qualitative factors such as consumer behaviour and willingness to pay. Also, the credit bureaus can evaluate and select the worthiness of the received credit.
Today, industries are now adopting the Internet of Things (IoT) based wearable technology, and these technologies pose grave privacy and security risk about the data transfer and the logging of data transactions. In healthcare, security and privacy threat are endangering the patient’s life.
Machine learning algorithms help insurance firms to identify cases that pose a high risk, potentially reducing claims and improving profitability. The output can help in improving the underwriting process claim processing assisting agents in sorting through vast data sets that insurance companies have collected.
AI and ML could assist banks in optimizing margin value adjustment, thereby reducing the initial margin for derivatives. Machine learning algorithms find the best combination of the initial margin reducing trades at a given time based on the degree of initial margin reduction in the past under different combinations of those trades
Insurers can now use new machine learning algorithms which can be used to check the claim registrations and claim settlements; this automation of the claim process is useful in the customer experience as it can reduce the claim settlement time.
To attend the diverse needs of the global clients, dedicated pool of resources, managed by Customer and local offshore project and account manager, research and support team.
To attend the diverse needs of the global clients, dedicated pool of resources, managed by Customer and local offshore project and account manager, research and support team.
To attend the diverse needs of the global clients, dedicated pool of resources, managed by Customer and local offshore project and account manager, research and support team.
To attend the diverse needs of the global clients, dedicated pool of resources, managed by Customer and local offshore project and account manager, research and support team.
To attend the diverse needs of the global clients, dedicated pool of resources, managed by Customer and local offshore project and account manager, research and support team.
To attend the diverse needs of the global clients, dedicated pool of resources, managed by Customer and local offshore project and account manager, research and support team.
To attend the diverse needs of the global clients, dedicated pool of resources, managed by Customer and local offshore project and account manager, research and support team.
