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Advanced analytics and the Finance functionXavier Fernandes
AgendaWhat is AI?
How is it affecting the insurance industry?
How is it affecting the finance function?
What it means for you
My background
• Defence industry – command & control systems – development & design
• Technology consulting
• M&A advisory – due diligence & post-merger integration
• Analytics director at Metapraxis – application of advanced statistical & machine learning
What is AI?
We humans keep moving the goalposts as soon as a machine achieves an outcome:
• Winning a game of chess against a Grandmaster
• Understanding speech
• Driving a car
What is AI?
AIArtificial Intelligence: exhibits behaviour which appears intelligent
MLMachine learning: enables programs to learn through training, instead of being programmed with rules
DLDeep learning: emulates the way animals’ brains learn subtle tasks
AI as “idiot savants”
• Learning from vast sets of historical data
• Seeing patterns in data
• Basic reasoning
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Machine learning… how does it work?
AI and the (current) insurance value chain?
• Compile & process richer sets of customer data
• Detect non-linear patterns in data
• Project outcomes with greater accuracy & precision
Risk assessment
• Reduce claim processing costs
• Analyse images and classify claims
• Predict settlement costs
Claims processing
• Assess individual claims for fraud indicators
• Identify networks of fraudulent activity
• Inform risk assessment & quotation stage
Fraud detection
• Chatbots utilise NLP to direct customer requests
• Automated responses in natural language
• 24/7, scalable solutions
Customer services
AI as a disruptor to the insurance value chain• Privately-owned cars = convenience, spontaneity, and comfort
• But the average car spends > 95% of time parked
• Autonomous vehicles + transportation-as-a-service = radical change …?
Global insurance premium
42% Car insurance
87% Personal lines
~40% Personal lines
How is AI affecting the finance function?
Robotic process automation (RPA) uses existing IT systems to mimic the actions of human experts –but tirelessly, and with consistency
Accounting
• Automating complex journal entries
• Performing and documenting account reconciliations
• Calculating and applying allocations
• Maintaining fixed-asset accounts
Accounts payable
• Entering nonelectronic-data-interchange invoices
• Performing 2- and/or 3-way invoice matches
• Processing expense-approval requests
• Completing audits (eg, duplicate supplier payments)
Accounts receivable
• Generating and validating invoices
• Applying cash to outstanding balances
• Analysing and processing disputes
• Creating reports (eg, accounts-receivable aging, credit holds)
Financial planning and analysis
• Building standard management reports
• Consolidating and validating budget and forecast inputs
• Gathering and cleaning data for analysis
Payroll
• Flagging time-sheet errors and omissions
• Auditing reported hours against schedule
• Calculating deductions• Harmonising data across
multiple timekeeping systems
Other
• Preparing external-reporting templates
• Conducting transaction audits of high-risk areas
• Preparing wire-transfer requests
Data analytics and the FP&A function
• Less straightforward to automate are activities that finance functions undertake to support strategic decision-making and – tracking
• But finance teams can still take advantage of analytics techniques to augment their own skills and experience
Complexity
Val
ue
Descriptive Analytics
What happened? Diagnostic
Analytics
Why did it happen?
Predictive Analytics
What will happen? Prescriptive
Analytics
How can we make it happen?
Data analytics and the FP&A function
Analytics type Answers … Tools used When to useDescriptive analytics What happened? Data aggregation Summarising business results
Diagnostic analytics Why did it happen? Data mining Drill down to root cause Uncover hidden variance Identify and isolate outliers
Predictive analytics What will happen? Statistical models Simulations Visualisations layering
historical, actuals & forecast
Educated estimates of the future
Prescriptive analytics How can we make it happen? Value driver models
Optimisation models
Strategic decision modelling Complex or time-sensitive
decisions When you have the ability to
change (some) drivers
Value of data and analytics
TRUST
in data authenticity and in analysis quality
LITERACY
of your workforce in data and analytics
COMPLEXITY
of data collection and timely response
DIVERSITY
of data sources, algorithms and skills
Any questions?
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