1.3 Evolution Of Banking + Harnessing Of Technology Flashcards
World oldest bank
Monte dei Paschi de Siena
(1472)
Operates in Italy till this day
1472-1980
Historical, traditional banking, first ATM opened in 1967
1980-2007
Self services, home and online banking, access to bank services outside the usual bank hours
2007-2018
Mobile banking, fintechs, digital only banks
2017 onwards
Real time banking everywhere
Digital innovations
Uks Financial Conduct Authority 2022 review:
Innovation is a risk to vulnerable customers who still want to use ATM/branch
Weakening in historic advantages of large banks by digital innovation
Three key trends that is continuing to reshape the competitive environment:
- shift from branches to innovation
- consumers demanding more digital offerings
- digital attackers taking a significant share of tradition banks revenue streams
Banks becoming more technology advanced by:
- giving employees continuous training
- assessing the skills required of their staff
- reviewing their organisational structures
- digitising their core processes
Technology that are having a major impact on banks and and their customers:
- open application programming interfaces (APIs)
- advanced analytics/AI/machine learning
- Conversational user interfaces (CUIs) chargers, voice user interface
- internet of thing (IOT)
Open APIs
Allows different applications to communicate and interact with each other.
Customers own their financial data and can share it with other providers and have instant access to a wide range of options and services.
Gives customer more choice and control on what to do with their money.
API cheaper and faster than traditional card based payments
Advanced analytics
Information technology (IT) that arw used to gather, examine and analyse data.
Banks can use this to predict customer behaviour and presences
Four part of advanced analytics solutions for banks
Descriptive
Diagnostic
Predictive
Prescriptive
Descriptive analytics
‘What happened’
(simple type of analytics)
- convert raw data into meaningful information
E.g. bank can use this to see how many customers are using their products
Diagnostic analytics
‘Why did this happen’
Used for processing and summarising the information gathered from the 1st stage
Identify and compare patterns/trends and show correlation where these exist
Predictive analytics
‘Why might happen in the future’
Use historical date and patterns identified to make informed predictions on what could happen in the future
E.g. how customers may act