Data management in financial companies: Challenges and opportunities
Posted: Tue Jan 21, 2025 4:24 am
Learn about the opportunities and challenges of data management in the financial sector. Discover the use of unstructured information.
With digital advances and the development of new technologies such as cloud computing, artificial intelligence and machine learning, financial institutions have various tools to offer better service to their clients and provide more efficient solutions to the operations they carry out.
These include the adoption of unstructured information in processes such as granting credit to users and companies, a promising alternative but also with the risk involved in using data other than the most conventional ones such as accounting balances and financial reports.
What are the main challenges for the sector in general and for credit decisions?
A significant 95% of companies identify unstructured self employed database management as a challenging issue.
Source: Techreport
The challenges of data management in the financial sector
Financial data management is a set of processes and policies that enable the consolidation of financial information, compliance with accounting standards and laws, and the production of detailed financial reports.
The main challenges with risks and opportunities for the sector include:
Customer focus and digital engagement: Customers are increasingly demanding personalization from businesses. Granular data management can deliver integrated experiences and hyper-personalized services and enables integration and sharing of customer data across functions.
Growth agenda: focus on value-generating initiatives not only by unlocking efficiencies, but also by projecting new business models, diversifying product portfolios and driving innovation.
Regulatory Reporting: Regulators are becoming more sophisticated and financial institutions will need to meet requirements and expectations, support changing compliance objectives and drive efficiency in reporting processes.
Data quality and resilience: Data accuracy early in the lifecycle and defining reporting metrics seek to demonstrate data health to senior management. Cyber risk remains a top priority.
Modernizing data infrastructure: Embracing new technologies includes implementing public cloud, fostering agility, and supporting open banking and financial services. Deploying a combination of technology, tools, and data based on the highest priority use cases will be critical.
97.2 % of organizations are investing in Big Data and artificial intelligence.
Source: Techjury
Big Data's contribution to credit scoring
Credit scoring methods are algorithms that evaluate the credit risk of a financing applicant between “good” and “bad” according to their probability of default. To do so, credit risk models use two types of variables as input: accounting numbers published in financial reports and information from financial markets such as stock returns and debt prices.
With digital advances and the development of new technologies such as cloud computing, artificial intelligence and machine learning, financial institutions have various tools to offer better service to their clients and provide more efficient solutions to the operations they carry out.
These include the adoption of unstructured information in processes such as granting credit to users and companies, a promising alternative but also with the risk involved in using data other than the most conventional ones such as accounting balances and financial reports.
What are the main challenges for the sector in general and for credit decisions?
A significant 95% of companies identify unstructured self employed database management as a challenging issue.
Source: Techreport
The challenges of data management in the financial sector
Financial data management is a set of processes and policies that enable the consolidation of financial information, compliance with accounting standards and laws, and the production of detailed financial reports.
The main challenges with risks and opportunities for the sector include:
Customer focus and digital engagement: Customers are increasingly demanding personalization from businesses. Granular data management can deliver integrated experiences and hyper-personalized services and enables integration and sharing of customer data across functions.
Growth agenda: focus on value-generating initiatives not only by unlocking efficiencies, but also by projecting new business models, diversifying product portfolios and driving innovation.
Regulatory Reporting: Regulators are becoming more sophisticated and financial institutions will need to meet requirements and expectations, support changing compliance objectives and drive efficiency in reporting processes.
Data quality and resilience: Data accuracy early in the lifecycle and defining reporting metrics seek to demonstrate data health to senior management. Cyber risk remains a top priority.
Modernizing data infrastructure: Embracing new technologies includes implementing public cloud, fostering agility, and supporting open banking and financial services. Deploying a combination of technology, tools, and data based on the highest priority use cases will be critical.
97.2 % of organizations are investing in Big Data and artificial intelligence.
Source: Techjury
Big Data's contribution to credit scoring
Credit scoring methods are algorithms that evaluate the credit risk of a financing applicant between “good” and “bad” according to their probability of default. To do so, credit risk models use two types of variables as input: accounting numbers published in financial reports and information from financial markets such as stock returns and debt prices.