AI and data science have a bright future: Ankit Sinha, VP of cloud practices and consulting, Searce
Speaking to TechGraph, Ankit Sinha, Vice President (VP) of Cloud Practices and Consulting, Searce said, “Artificial intelligence and data science have a bright future because of the advanced automation they offer. for various applications that we use daily.
Read the full interview:
TechGraph: How does Searce use its industry expertise and digitalization to deliver data-centric research and analytics services to its clients?
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Ankit Sinha: The data and analytics practice at Searce has many years of experience designing and implementing multi-terabyte, multi-user databases and data warehouses supporting reporting platforms and analysis.
We use our industry expertise with the most appropriate methods and our Ips/Accelerators/Framework and Cloud Modernized digital tools to provide data-centric research and analytics services to our clients. For example, integrating your business data is key to performing retail data analysis effectively.
Big Data and advanced analytics are playing a major role in the future of retailers by helping them make smarter decisions, improve operations and increase sales. And so, Searce has built a Customer Data Platform (CDP) for our retail customers that will help them see business values faster.
Searce’s industry experts understand customer data, analyze it, and use cutting-edge technology to create relevant business solutions. We have a dedicated team for each industry that creates unique solutions with our hyper-scale.
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To cite another example, Searce has pre-built AI/ML models to address all the pain points of our Fintech clients to accelerate their digital journey by improving their core business processes and creating customer-centric experiences. . Searce collaborates with data experts from client-side services teams in two ways.
First, we work with their partners to leverage the enterprise and project data architectures they have defined to identify asset datasets and associated asset data stores that can be used to perform data analysis where we discover new data assets during our projects. . To improve analytical services, we integrate the efficient AI and ML algorithms with data management and analysis
Second, we will work with their client-side service teams to help them use asset data analytics to frame the business objectives, scope and business cases that have been identified.
Additionally, we will apply our innovative best practice framework for use case selection to help your client-side delivery partners identify adjacent use cases and operational business opportunities that are directly related to security, reliability, affordability and growth in sectors/industries. With the customer’s business needs in mind – Searce builds compelling customer data platforms and recommendation engines to accelerate business.
TechGraph: How are AI and ML contributing to the company valuation revolution towards financial inclusion?
Ankit Sinha: Many key processes and functions of FinTech companies are supported by AI/ML. Using advanced machine learning models for credit scoring has become an integral part of underwriting processes for many fintech lenders.
This improves application processing efficiency, enables faster application processing, and improves portfolio quality, as powerful ML models predict default risk quite accurately.
AI and machine learning have been extremely effective in helping fintech players in the MSME lending space, especially those at the bottom of the pyramid who have limited or no access to formal credit from banks and others. lenders.
To compensate for the lack of credit history, fintech players are looking to design innovative methods for assessing creditworthiness. The Custom ML model built at Searce deviates from the traditional constraints of data requirements, allowing lenders to fairly accurately predict the probability of default/default based on various types of alternative data and to assess and underwrite risk. credit accordingly.
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To build these AI and machine learning models, some fintech lenders lend to micro and small businesses using localized economic data, corporate image data (e.g. stock of goods, store space, facade store, location, etc.), as well as limited banking data. , informal accounting data from mobile apps, etc.
TechGraph: How does Searce leverage technology to improve customer experience?
Ankit Sinha: “What gets measured gets done”, goes the old adage. Over the years, leaders looking to improve their organization’s competitiveness have devoted a great deal of attention to a wide variety of metrics ranging from assessing the level of customer satisfaction, metrics to determine whether a business should continue a product, metrics to gauge how effectively a company serves its customers, and even metrics to gauge how “ideal” a customer’s experience is.
Our experience suggests three essential elements that can transform an intermediate approach to measuring customer experience into one capable of having impact and creating value. Effective measurement of customer experience initially focuses on the journey level rather than analyzing transactional touchpoints and overall satisfaction.
Second, investing in hardwired technology that can capture customer feedback daily from multiple channels and integrate survey results, social media posts and operational data into comprehensive dashboards is essential. and role specific. Transparency can be created and decisions can be made at all levels.
The final step to overcoming organizational inertia is to cultivate a mindset of continuous improvement throughout the organization. Customer feedback should be incorporated into frontline worker responsibilities and used to improve the customer experience.
TechGraph: How do you see the technologies, namely AI, ML and data science, in terms of their relevance on the analytics network? What does the future look like?
Ankit Sinha: A variety of business and consumer benefits arise from data science, machine learning and artificial intelligence. In recent years, research and development efforts on automated processes and machine learning have intensified, with AI and data science automating much of production, thereby increasing efficiency.
Data science and AI will become more and more popular in 2022. We can observe this trend by following the development of hyper-automation and advanced natural language processing. Additionally, augmented analytics will take advantage of concepts such as the Internet of Things to enhance and enhance various technologies, such as advanced analytics, user interface, and cybersecurity.
The future of AI, machine learning and data science will be further established by more machines, devices, services, smart cities and homes powered by ML and AI. We will focus our efforts on the development of more efficient human-computer interaction, as well as the development of true autonomous systems capable of performing complex tasks for long periods of time without human assistance.
The future envisions encompassing these technologies in various industries. Artificial intelligence and data science have a bright future due to the advanced automation they offer for various applications that we use daily. With the rise of AI, IT companies, banking companies and other businesses will benefit from increased productivity, speed and resolution.
To make full use of this holy grail of modern technology that is machine learning, companies in all kinds of industries are jumping on the artificial intelligence bandwagon. As a result, research can progress faster, leading to improvements for both consumers and producers.
TechGraph: What’s the response so far for your consulting services?
Ankit Sinha: In line with the industry trend, we have seen strong growth in demand for our IT consulting services over the past two years. IT managers are constantly looking for ways to reinvent their operational and business models using technology as their backbone. And with this change.
Some of the main challenges we face today for our clients using our consulting services include:
a. Help organizations see improved employee productivity and collaboration, whether teams continue to operate from an office, satellite offices, or remotely
b. Helping our clients design their IT systems to be able to handle the dynamic business environment
vs. Extract maximum value from customer data that operates in silos to help them on their journey to becoming a truly data-driven organization
D. Leverage IT agility, i.e. the ability to design and deploy changes to the IT environment at low cost, with minimal return on investment and risk
e. Optimize their spending on IT environments while offering them the possibility of accessing the most innovative solutions and platforms.