- Sensors and agents, such as social media listening platforms, horizon scanning, and early warning systems.
- Integrated data warehouses and big data technologies such as Hadoop
- High frequency reporting and continuous control monitoring
- Crowdsourcing risk information
- Visual data discovery, optical character recognition and natural language processing
- Scenario planning and stress testing
- Risk analytics and machine learning
Algorithm | Purpose | Examples |
Regression algorithms | Iteratively refine a model of relationships between variables | ordinary least squared regression (OLSR), linear regression, Logistic regression, stepwise regression, multivariate adaptive regression spines (MARS), and locally estimated scatterplot smoothing (LOESS). |
Instance based algorithms | Learn from example data and compare to new data – including | k-nearest neighbour (kNN), learning vector quantization (LVQ), Support Vector Machines (SVM), self-organising map (SOM) and locally weighted learning (LWL). |
Regularisation algorithms | Simplify other models (such as regression) to improve generalisation | Ridge regression, least absolute shrinkage and selection operator (LASSO), elastic net, least angle regression (LARS). |
Decision tree algorithms | Construct a model of decisions made based on attributes of the data | classification and regression tree (CART), Linear temporal logic (LTL) Checker, iterative dichotomiser 3 (ID3), C4.5, C5.0, chi-squared automatic interaction detection (CHAID), decision stump, M5, conditional decision trees |
Bayesian algorithms | Calculate the probability of an event | naïve bayes, guassian naïve bayes, multinomial naïve bayes, averaged one dependence estimators (AODE), Bayesian belief network (BNN), Bayesian network (BN), Hidden Markov Model (HMM), Markov Chain Monte Carlo |
Clustering algorithms | Use the inherent structure in the data to organise into groups | k-Means, k-Medians, Expectation Maximiser (EM), Hierarchical Clustering. |
Association Rule Learning algorithms | Extract rules that best explain observed relationships in data | Apriori, and Eclat |
Artificial Neural Network Algorithms | Perceptron, Back-Propagation, Hopfield Network, Radial Basis Function Network (RBFN) | |
Deep Learning Algorithms | For more complex and large datasets | Deep Boltzmann Machine (DBM), Deep Belief Networks (DBN), Convolutional Neural Network (CNN), Stacked Auto-Encoders |
Dimensionality Reduction Algorithms | Seek and exploit the inherent structure in the data to summarise or describe the data | Principal Component Analysis (PCA), Principal Component Regression (PCR), Partial Least Squares Regression (PLSR), Sammon Mapping, Multidimensional Scaling (MDS), Projection Pursuit, Linear Discriminate Analysis (LDA), Mixture Discriminate Analysis (MDA), Quadratic Discriminate Analysis (QDA), Flexible Discriminate Analysis (FDA) |
- Discover processes from logs
- Detect process inconsistencies between different groups or applications
- Perform rule-based checking (such as SLAs)
- Identify trends and make forecasts
- Predict the likelihood and impact of an event
- Classify data and build decisions trees
- Visualise data
- Discover key risk indicators
- Analyse stresses and scenarios
- Identify typical problems or common solutions
- Generate treatment options – the risk of action, inaction, over-reaction, and under reaction
- Machine learning (as described above)
- Simulation models
- Stochastic Optimisation models
- Artificial intelligence, and
- Business Process Management (BPM)
- Machine Learning
- Business Process Management (BPM), and
- Case management, through an integrated GRC
- Using a full stack vendor and implementing technologies based on their roadmap – if you are prepared for potential lock-in.
- Picking a best of breed solution that includes BI, BPM, GRC and an application development platform – if you can invest in the necessary skills.
- Pick a BI platform only – if you have sufficient data management and integration tools and skills