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Banking & Data Science

Banking and financial industry once considered an impenetrable domain of a few is now under real threat from “FinTech”. The industry is going through a major disruption, disintermediation and digital innovation. Caught between increasing strict norms and reducing costs through consistent innovation, the older powerhouses are on a shaky platform. How do the traditional banks and finance institutes get rid of older rigid methods and adopt a data driven culture – Data Science is the answer.

Data Science can help in great ways for banks and financial institutions, including economic advisories to identify patterns in markets and predictions. Getting best use of data for their benefits. Since banks are also apprehensive about sharing their data with external companies, they are wanting to develop in house capabilities to manage such large chunks of data to curate, analyse as well as predict and offer tailored/customized finial products, in lieu of this offering better services resulting in customer delight.

Why Data Science cannot be ignored
  • Traditional Banks: A Rigid Culture, Shackled for Funds: When compliance was the king, a rigid banking culture was at bloom. Global IT Spending, according to Gartner is down to -4.9% in the financial sector. With limited IT budgets banks need to move away from too much focus on compliance albeit concentrate on digital transformation, innovation and collaboration.
  • Lending and Paybacks: Credit Management (Lending & Paybacks) is the key pillar of banking industry. Consumers will always need credit and will always need to pay back to the lending institution. The importance of credit management worldwide makes it a primary focus for opportunistic lending firms. Without Data Science implemented in the organisation, it would be very hard even to capture a small slice of this huge pie.
  • Rebuilding the Core: Let’s get back to the drawing board. The banks need to retool their core banking. They need clean data in a reliable architecture to mitigate operational risk and meet their reporting and stress test requirements. Banks are now trying to adopt open source model as traditional systems, architecture, current databases, middleware, messaging cannot cope up with rising magnitude of data.
  • Data Science is the innovation that every finance institute needs to have a serious thought about. However, there are multiple challenges that these firms need to address before they can leverage it.
  • Lack of Specialists: Banking and Financial institutes need specialists and not generalists. A bit like medicine, after basic studies, deep diving for specialisation is must.
  • Begin with End Goal: It is important to have a mental goal before one touches the data. Without a mental goal the behemoth data can throw off, the young and lesser experience data analyst or the aspiring data scientist.
  • Scarcity of Cross Departmental Expertise: In addition to the deep technical foundation, cross domain expertise is a must. Currently the availability of such cross departmental expertise is scarce.
  • Rare availability of talent that can articulate: A data scientist who cannot articulate what the model does and why it is of value to the business stakeholders has a tough path to success.

Data Science: Linking & Solving the Riddle of Enterprise Data & Big Data

Current data management landscapes often fail to create a link between the Enterprise Data and Big Data. This makes it difficult to operationalise Data Science and derive valuable insights. Users struggle to massive haystacks of data to find hidden needles of worthy insights.

Enterprise Data:

  • High quality, structured data.
  • Contracts, customer information and financial transactions.
  • Clear governance, security concepts and life cycle management practices.

Big Data:

  • Characterised by high volume of semi-structured or unstructured data.
  • Captured in Data Lakes- Hadoop.
  • Lack comparable enterprise governance, security and lifecycle management.

Advantages of an upskilled Workforce

  • The current financial resource is largely working in Siloes and handling limited aspects of data. An upskilled resource is effectively able to implement best practices of Data Science and provide worthy insights.
  • Data Analysts, Big Data Resources with cross departmental experience within the organisation have an excellent chance of upskilling into an effective Data Scientist.

How Can SkillUp Technologies help you leverage Data Science?

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